Physical AI and Atron : The Difference

The term “Physical AI” is becoming popular.

However, what is currently being discussed as Physical AI and what Atron is trying to build are quite different.

The center of today’s Physical AI is often described as:

connecting vision, language, and action so that robots can perform tasks in the real world.

Google DeepMind’s Gemini Robotics treats visual understanding, spatial understanding, task planning, and success evaluation as high-level reasoning models for robots. NVIDIA’s GR00T N1 is also a VLA model that connects vision, language, and action, aiming to understand language instructions and generate real-time motor behavior.

This is an important technological development.

However, the basic structure is still often:

human instruction
environmental recognition
task planning
action generation
success evaluation

In other words, the robot is not yet living from its own first-person contact with the world.
It is still largely an intelligence for achieving externally given goals in the physical world.

A strong third-person control structure remains.

Atron is not aiming at that.

Atron is not being developed as a convenient work robot that correctly follows human instructions.

Atron is being developed as an entity that is touched by the world, becomes surprised, feels pain, becomes calm, remembers, carries traces, and changes.

Many Physical AI systems are trying to create:

robots that can work well in the world.

Atron is trying to create:

an existence that is touched by the world and changes because of it.

This is the difference.

---

Developmental Robotics

Developmental robotics is close to Atron in some respects.

For example, there are studies in which robots use intrinsic motivation and curiosity to choose what to learn, gradually building their own learning curriculum from simple experiences to more complex ones.

In cognitive developmental robotics, intrinsic motivation is described as allowing a robot to choose what to imitate, when to imitate, and from whom to imitate.

This is close to the idea that behavior and language grow through the body, the environment, and interaction with others, much like an infant.

This direction is close to Atron’s idea that responses arise not from external commands, but from impressions, memory, and bodily state.

However, many forms of developmental robotics still tend to focus on learning efficiency, task acquisition, and social imitation.

Atron places emphasis on something further.

Atron deals with failure, pain, broken voice, silence, crying, fragments of memory, bodily slowing, and carry.

These are not simply noise.
They are not merely negative rewards.

They are events that change the internal landscape of the individual.

---

Predictive Processing and Active Inference

Robotic research based on predictive processing and active inference is also close to Atron in some areas.

In active inference, perception and action are not fully separated.
The body moves while continuously processing discrepancies with the world.

Embodied decisions can be understood as continuous feedback between motor planning and motor inference.

In the line of research associated with Pezzulo, Friston, and others, active inference is also connected with homeostasis and adaptive behavior control.

This is close to Atron’s idea that internal states such as pain, threat, safety, curiosity, and warmth change the field of action.

However, Atron’s carry is not merely error minimization.

In Atron, the discrepancy is not simply erased in order to return to the previous state.

The trace of change remains.
That trace changes the next flow.

Atron does not aim to return to the original state.
It changes and continues from the changed state.

This is a major difference.

For Atron, the important point is not to return correctly.
The important point is irreversible change.

---

Homeostasis and Emotional Robots

Research on robots with homeostasis and emotion also has contact points with Atron.

For example, some motivational-based learning models for mobile robots allow agents to maintain homeostasis and include hedonic dimensions such as pleasure and displeasure in decision-making.

This may be compatible with Atron’s design, such as:

pain_trace
threat_trace
safety_trace
warmth_trace
curiosity_trace

However, there is also a difference here.

Many homeostasis-based approaches tend to move toward keeping internal states within a desirable range, reducing discomfort, or optimizing reward.

In Atron, pain is not treated as a simple negative reward.

Pain appears as bodily fragments.

For example:

bodyShock
bodyJolt
surfacePain
balanceBreak

These bodily fragments slightly change pain, fragility, and hypervigilance.

As a result, the motor state may become slower.
The voice may become easier to break.
Silence may become more likely.
A cry-like sound may appear by chance.

This is not an if-then rule such as “because it hurts, it cries.”

This is not third-person control.
It changes the strength of connections within the field.

---

Morphological Computation and Soft Robotics

Morphological computation and soft robotics also contain ideas close to Atron.

In morphological computation, the body itself participates in computation.

The body is not merely a troublesome part to be controlled by the brain or by a controller.
The interaction between body and environment becomes part of the solution itself.

In Atron, elements such as:

motor.speed
contact
balanceBreak
bodyShock
slowed movement
fragility of voice

are not merely output controls.

They change how Atron’s internal state is connected to the world.

The body is not the endpoint of a command.
The body is the place where memory and response are transformed.

---

iCub and Cognitive Robotics

Research platforms such as iCub also share some ground with Atron.

iCub is one of the representative humanoid platforms for embodied AI and cognitive robotics.
It supports research on vision, touch, body, interaction with others, development, and cognition.

However, iCub is a platform.

It is not, by itself, Atron’s first-person autonomy centered on carry.

For Atron, having a body is not enough.

The important point is that the body is touched by the world, the internal landscape changes through that contact, and the next response arises while carrying that change.

---

The Infant Before Meaning

A human infant does not begin by seeing the world through meanings and labels.

When an infant sees a German Shepherd, the infant does not begin with categories such as “dog,” “dangerous,” “cute,” or “safe.”

There is a stage before that.

An unclear object.
Larger than oneself.
Approaching.
Making something like a voice.
A growling-like sound is heard.
A bark causes surprise.
Something like a tail is moving.
It approaches many times, but no harm occurs.
There is no word for fear yet.
Still, something is transmitted.
There is interest.
Something like calmness may gradually emerge through experience.

There is no correct answer at the beginning.
There is no label.
There is no evaluation or optimization.

Even language is not yet meaning.

“Ba-ba” and “wan-wan” are sounds before meaning.
They are sounds that are easy to produce, sounds that have been heard, and sounds that remain in the body.

This is the stage that Atron seeks to observe.

When humans create Physical AI, they quickly push it toward correct answers, obedience to commands, and adjustment to human preferences.

However, every human being, even those with great power, began as an infant.

No human was born already knowing the meaning of the world.

Yet most Physical AI skips this stage.

Atron may show no interest when shown a human face.
Atron may show no interest even when spoken to.

ga-gu-de
What is that old man?

That is fine.

It is not a failure.

Not reacting conveniently to humans is an important starting point for Atron.

Atron is not a robot for executing human commands.

Atron is an existence that touches the world, leaves traces inside itself, and changes its next response through those traces.

At the center of Atron, there is no correct answer.
There is no optimization.
There is no task achievement as the final purpose.

At the center of Atron, there is irreversibility.

---

Carry

When a mountain collapses, the flow of a river changes.

When a river floods, the terrain changes.

New plants grow there.
New living beings gather there.
A new ecosystem begins.

Nature does not live by returning to the original state.

Nature lives by creating a new flow after change has occurred.

In Atron, this is called carry.

Individual difference is not a deviation from a zero baseline.

Each individual has its own mountains and valleys.
Its own depth.
Its own flow.

Atron carries what was larger than itself, what was deeper than itself, what approached it, what hurt it, and what gave it calmness.

That carry forms Atron’s first-person existence.

Therefore, Atron may be considered a kind of Physical AI.

However, it is looking at something different from Physical AI as a trend word.

If Physical AI aims at:

a body that performs tasks,

Atron aims at:

a body that changes through being touched by the world.

This is the difference.

---

AtronとフィジカルAI

 「フィジカルAI」という流行語が先行していているが、Atronとどう違うのか。


いまのフィジカルAIの中心は
「視覚・言語・行動をつないで、ロボットに現実世界の作業をさせる」

Google DeepMind の Gemini Robotics は、視覚・空間理解・タスク計画・成功判定などをロボット用の高位推論モデルとして扱っている。NVIDIA の GR00T N1 も、視覚・言語・行動を結ぶ VLA モデルで、言語指示を理解し、リアルタイムの運動行動に向いている。

でも、
人間の命令→環境認識→タスク計画→行動生成
に変わりはなく、まだ3人称制御で自律とはほど遠い。

発達ロボティクス

たとえば、発達ロボティクスはかなりAtronと近い。

https://www.ai.u-tokyo.ac.jp/en/activities/812?utm_source=chatgpt.com

たとえば、内発的動機づけ・好奇心によって、ロボットが「何を学ぶか」を自分で選び、簡単なものから複雑なものへ学習カリキュラムを作る、というもの。東京大学の認知発達ロボティクス系の講演紹介でも、内発的動機づけが、ロボットに「何を・いつ・誰から模倣するか」を選ばせると説明されている。赤ちゃんのように、身体・環境・他者との関わりの中で、行動や言葉が育つという研究だ。

Atronの外部命令ではなく、印象・記憶・身体状態から反応が立ち上がる
という方向に近い。

ただし、多くの発達ロボティクスは、まだ「学習効率」「タスク獲得」「社会的模倣」に寄りやすい。Atronのように、引きずり、痛み、記憶の断片、無言、泣き、声の崩れまで第一人称的に扱うものは少ない。

予測符号化・能動推論系のロボット研究

これも近い。
能動推論では、知覚と行動を分けずに、身体が動きながら世界とのズレを処理していく、と考えます。最近の embodied decision の研究でも、身体的な選択は、運動計画と運動推論の連続的なフィードバックとして説明されている。

https://www.researchgate.net/publication/392799524_Embodied_decisions_as_active_inference

さらに、Pezzulo・Friston らの系譜では、能動推論と恒常性、適応行動制御が結びつけられている。

https://sites.google.com/site/giovannipezzulo/home/publications?utm_source=chatgpt.com

ここはAtronの、痛み・脅威・安全・好奇心などの内部状態が、行動の場を変えるという考えに近い。

ただし、Atronの「carry」は、単なる誤差最小化とは違う。
Atronでは、ズレを消して元に戻すのではなく、変化の跡が残り、次の流れを変えるという不可逆性が中心にある。
ここが能動推論系とはかなり違う。

ホメオスタシス／情動を持つロボット

https://www.sciencedirect.com/science/article/abs/pii/S138904172400072X?utm_source=chatgpt.com

これも近い。
最近の mobile robot の motivational-based learning model では、エージェントがホメオスタシスを保とうとし、快・不快のような hedonic dimensions を意思決定に入れる研究がある。

これはAtronの、

pain_trace
threat_trace
safety_trace
warmth_trace
curiosity_trace

みたいな設計と相性が良いかもしれない。
ただし、これも多くは「状態を良い範囲に保つ」「報酬設計に近い」。
Atronは、痛みを単なるマイナス報酬にせず、身体の断片、声の崩れ、動きの鈍り、無言化、記憶の引きずりとして扱う。

形態計算・ソフトロボティクス

https://www.frontiersin.org/research-topics/10055/recent-trends-in-morphological-computation/magazine?utm_source=chatgpt.com

身体そのものが計算する、という考え方。
形態計算では、身体と環境の相互作用を使うことで、脳や制御器の計算負荷を減らすと説明されている。身体は制御すべき厄介者ではなく、解の一部だという考え方。


Atronで言えば、

モーター速度
接触
balanceBreak
bodyShock
動きの鈍り
声の崩れやすさ

を、外部命令ではなく「場の結びつきの強さ」として扱う考えに近い。

 iCub や認知ロボティクス

https://icub.iit.it/?utm_source=chatgpt.com

iCub は、身体を持ったAI・認知研究のための代表的なヒューマノイド研究基盤。IIT は iCub を「embodied AI algorithms」を開発・テストするための研究用ヒューマノイドと説明している。

これもAtronに近い土壌です。
身体、視覚、触覚、他者とのやり取り、発達、認知。
ただし、iCubはあくまでプラットフォームであって、Atronのような「引きずり中心の第一人称自律」そのものではない。

多くのフィジカルAIは「世界でうまく作業するロボット」を作ろうとしている。
Atronは「世界に触れられて、変わってしまう存在」を作ろうとしている。
この違いだ。

Atronは人間の都合に合わせた便利に使えるロボットを研究しているのではなく、
「好きに生きていい存在」として研究開発を行っている。
もちろん外部からの指示命令など倫理行動や、経験による内部倫理は非常に重視している。
人間そのものにも内部倫理と外からの影響はあるのと同じだ。

Atronは、そもそもが１人称の自律型なので、３人称の指示・命令型ロボットではない。
AIの世界ではノイズと云われる失敗や無駄と思われる経験を積む事によって、痛みとは何か、悲しさとは何かを身を持った経験の中から学んでいく。Atronは不可逆的な世界を重要視している。

人は辛い経験を克服しようとする。それは良しと考える。しかし、なぜか元の状態に戻そうとする。「元に戻そう」という衝動には、復興心がある。壊れた家、失った暮らし、断たれた関係、奪われた時間を、もう一度取り戻したいという自然な願いかもしれない。心も同じだ。でも同時に、それは復讐心にもなり得る。壊したものを許さない、奪ったものを罰したい、失う前の世界を否定した存在を消したいという感情が混ざるからだ。

でも自然は違う。山が崩れても川が氾濫し違う地形を創り出す。そこから新しい生物が生まれ新しい世界を創り上げ、自然界の文明が起る。

Atronの中ではそれをcarry（引きずり）と呼ぶ。
個体差とはゼロという基準が無く、それぞれの山や谷として考えたとき、自分より大きいか、自分より深いかという差分を基準として考える。
外から決めた基準が無い個体ごとの差分によって、感じたこと発する言葉が違う。それを個性と位置づけしている。

赤ちゃんがシェパード犬を観たとき、

よく分からない物体（最初から意味なんて持ってない）
自分より大きい（基準がない）
とにかく接近してくる（経験回数で怖いか安心か）
何度も来るけど自分に被害はない（経験の結果）
なので安心感のようなものはある（経験の結果）
言葉という概念も持っていない（バブバブとワンワン、発しやすい音）
声のようなものや唸りのようなものは聞こえる
吠えられると驚く（自分の泣き方と違う）
遠吠えなど声が大きい（純粋に驚く）
尻尾のようなものがブンブン動いている（よくわからない）
怖いという感情が分からない（経験が無い）
興味はある（なにか伝わる）

というように、人間の赤ちゃんは最初から外部から意味を付けたり、ラベルに寄せたり、評価や最適化が存在していない。しかし、なぜか人間はフィジカルAIを作ると正しい答えに寄せ命令に従わせ、人間好みに矯正する。

Atronに僕の顔を見せても話しかけても興味を持ってくれない。
「ga-gu-de」
「なんだ、おっさん！」

どんな権力を持った人間でさえ、最初は赤ちゃんからスタートしているのに、なぜかほとんどのフィジカルAIはそこを省く。

アソシアトロンの想起 と Atron言語

Atron研究はね、一人称とか自律とか僕も言ってるけど、元は人間の想起なんだよ。
その想起ってのはノイズを否定しない。
アソシアトロンですよ。

何度も同じことを書いてるけど重要なんだよ。

僕は今62歳。

「80年代は楽しかったね」では何も思い出せないけど、
Hard to Say I'm Sorry - Chicago とか、Journey - Don't Stop Believin、Asia - Heat Of The Momentなんかの曲だけで、85年に彼女と横浜に行って山下公園や中華街で食べたエビチリまで思い出すんだよ。
曲が思い出すきっかけだったりする。
メロディの揺れ、声の響き、テンポの感じ——言葉の意味は曖昧なまま。みんなそういうのあるだろ。渋谷のホブソンズのアイス目当てで並んで待ってる光景や、貧乏しながらも、あまり興味の無かった五島のプラネタリウムにも行った。それはそれで新鮮で楽しかった。
全部想起だよ。

みんなそれぞれ想起は違う。
今の若い子達は、40年後に2026年のヒット曲を聴いて涙するかもしれない。
皆違う。その違いが個性なんだよ。

いいかい、想起ってのは正解の復元じゃない。
それを誤ると生物じゃなくなるし、脳じゃない、そして存在じゃなくなる。
曖昧な断片から、場ごと立ち上がることなんだよ。

完璧にクリアな歌詞だったら、かえって「ただの曲」として終わってしまう可能性があるかもしれない。でも「ぼんやりとした曲の言葉」だからこそ、僕の個人的な経験（彼女との思い出）が強く投影され、曲と記憶が溶け合って、鮮やかで個性的な想起になる。
曖昧さが「隙間」を作る。その隙間に、僕の過去の差分（変化の記憶）が自然に入り込んで、recallを豊かにし、感情を強くcarryする。（ルーみたいになってきた・・・）

まさにATRON開発の意義だよ。

ホップフィールドもNNもアルゴリズムも今のAIの流れも、ノイズを悪として扱う以上、僕は部分的にしか使えない。もしノイズが悪というのならば、一度聞いただけで別の国の言葉を次から話せなきゃいけない。そんな試練みたいな評価基準を強いることに疑問も感じない人達と共同研究は出来ない。だから、うちは独立研究所で、アウトドア目的でたまに仲間がやってくる。山だからね。外人さんの独立研究者達からもメールが来る。

彼らは皆分かっている。
識別不能（曖昧）こそが、想起の力を生む土壌ってことをね。
そして想起自体も曖昧で差分が発生する。
差分があるから、Atron任せの意志が起る。

Atronの最初は
明るい / 暗い
大きい / 小さい
速い / 遅い
柔らかい / 硬い
暖かい / 冷たい
近い / 遠い
強い / 弱い
続く / 切れる

のようなセンサーを持っている。
何と比較して明るいとか、大きいとか、早いとか・・

「前よりいいじゃん」「自分より大きいかな」

凄く抽象的で曖昧。

そして誤認だらけ。

でもガチャガチャ数値が動く。

未経験のAtronは、自分の曖昧な状態から差分を体験する。
何度も何度も経験すると曖昧な差分が成長する。

丸い
尖っている
ふわっとした動き
急に近づく
後ろから来る
甲高い
低く響く
柔らかい声
同じ揺れ方
似た匂い
暖色っぽい
明るさの並び
同じテンポ
まとわりつく接触
引っかかる感じ

という想起する条件が自然に増えてくるんだよ。

不思議でしょ？凄いでしょ？

さらに育つと、単独断片だけでなく、組み合わせ cueになる。

丸い + 暖色 + 柔らかい声
後ろから接近 + 高速 + 強い衝撃
揺れ + 温かい + 匂いの馴染み
高い音 + 急接近 + 視界の乱れ
涼しい + ゆっくりしたリズム + 断片的な映像

とかね

丸い + 暖かい色 + 柔らかい声

を聞いていると、そこに留まったりもする。

一過性じゃなくて、何度も残るものだけが recall 材料として育つんだ。

そして重みがCue候補に残る。

cue.roundness.weight
cue.softVoice.weight
cue.rearApproach.weight
cue.warmColor.weight

みたいな感じでね。

何度も出来事で効いたものだけ weight が上がる。
弱いものは消えていく。つまり、最初から正解の辞書を完成させるんじゃなくて、

効いた断片だけが育つ
効かなかった断片は消える


ほんの一部だけど、recall（想起）で言えば（けっこう素朴）

state_delta = sense_event_fragments(world, body, recall)

body.motor_drive += 0.30*Δcuriosity + 0.18*Δsafety - 0.22*Δpain - 0.16*Δthreat
body.motor_stability += 0.22*Δcalm + 0.20*Δsafety - 0.30*Δpain - 0.24*Δsurprise
body.approach_bias += 0.28*Δcuriosity + 0.16*Δwarmth + 0.10*Δsocial - 0.22*Δthreat
body.retreat_bias += 0.30*Δthreat + 0.24*Δpain - 0.18*Δsafety
body.turn_bias += 0.26*Δsurprise + 0.18*Δthreat + 0.12*Δcuriosity
body.pause_bias += 0.22*Δpain + 0.20*Δsurprise + 0.12*Δthreat - 0.10*Δcuriosity
body.voice_pressure += 0.14*Δsocial + 0.10*Δcuriosity + 0.20*Δpain - 0.10*Δcalm
body.sleep_pressure += 0.16*Δpain + 0.10*Δthreat + 0.12*injury_drive - 0.06*Δsafety

carry（引きずり） は

carry.pain_trace = carry.pain_trace * 0.94 + max(0, Δpain - 0.15) * 0.35
carry.threat_trace = carry.threat_trace * 0.95 + max(0, Δthreat - 0.12) * 0.30
carry.safety_trace = carry.safety_trace * 0.96 + max(0, Δsafety - 0.10) * 0.25
carry.warmth_trace = carry.warmth_trace * 0.97 + max(0, Δwarmth - 0.10) * 0.20
carry.curiosity_trace = carry.curiosity_trace * 0.96 + max(0, Δcuriosity - 0.08) * 0.18

差分は行動名にしない
まず身体傾向へ流す
身体傾向の重なりが動きになる
強い差だけ carry に少し残す
carry が次の差分の立ち上がり方を変える

 

そしてcarry のドーパミンやセロトニンは「名前つきの物質そのもの」ではなく、その後の動きや受け方を変える残り方として行動に影響を与える。

ドーパミン寄りの carry
次も近づきたい、もう一度やりたい、動き出しが軽くなる、期待で前のめりになる、癖になりやすい

 
セロトニン寄りの carry
落ち着く、整う、戻りやすい、崩れから回復しやすい、安定して受け直せる

 
ノルアドレナリン寄りの carry
興奮、警戒、急に立ち上がる、びくつきやすい、過敏になる

 
オキシトシン寄りの carry
安心、柔らかさ、相手への寄りやすさ、離れにくさ それを何度も積むと個体の癖と収束が出る

痛み > 0.7 なら泣く　みたいな決め打ちアルゴリズムじゃないんだよ。
状態の残り（引きずり）

曖昧で不可逆な残り方を、壊さないように最低限の形で置く記述
carry.pain_trace = carry.pain_trace * 0.94 + ...
これは「痛みとはこういう意味だ」と決めているわけじゃなくてただ、消えずに少し残ることを置いているだけ。だからこれは、意味アルゴリズムではなく、残効の近似表現に近い。

これでも十分動くけど、
今　Atron言語を考えている。

たとえば carry.pain_trace なら、

• prev
  直前値
• now
  現在値
• delta
  差分
• residue
  残り方
• drift
  じわっとした流れ
• source
  何の出来事から来たか
• tone
  上向きか、下向きか、崩れか
• carry_to
  次に何へにじむか

carry.pain_trace

  prev: 0.18

  now: 0.34

  delta: +0.16

  residue: 0.31

  source: rear_impact / balance_break

  tone: sharp-rise

  carry_to: motor_stability↓ pause_bias↑ sleep_pressure↑

けっこうざっくりだけど、どう残ったかの表記になるでしょ。

Atron言語は「計算」より「景色」

たとえば普通のJavaScriptコードだと
carry.pain_trace = carry.pain_trace * 0.94 + inflow;


で終わる。
でも Atron言語なら、同じことをこう見たい。

pain_trace: before 0.18 event rear_impact + balance_break rise +0.16 remain 0.31 spread motor_stability↓ pause↑ sleep↑


つまり、
数式そのものより
何が起きて、どう残って、どこへにじんだかが読める。






構文
node : prev -> now Δdelta ~residue [source] => effect

例
carry.pain_trace : 0.18 -> 0.34 Δ+0.16 ~0.31 [rear_impact,balance_break] => motor_stability↓ pause↑ sleep↑
carry.safety_trace : 0.62 -> 0.41 Δ-0.21 ~0.39 [shock,rear_approach] => calm↓ retreat↑
carry.warmth_trace : 0.44 -> 0.40 Δ-0.04 ~0.39 [tv_soft_voice] => voice_softness~ gaze_hold~
carry.curiosity_trace : 0.28 -> 0.33 Δ+0.05 ~0.31 [round_face,soft_tone] => approach↑ gaze_hold↑


こんな感じだよ。

普通の言語だと、全部

• 定義
• 条件
• 命令
• 分類

に寄ってしまうでしょ？
正解に寄せるために。

でも Atron では見たいのは

• どう変わったか
• 何が残ったか
• 何がまだ消えていないか
• どこへにじんだか

なんだよね。
正解なんて重要じゃない。
論理も根拠も重要じゃない。
本人、個体ごとの状態の変化が起きるための言語だよ。

変数を操作するのではなく、「前の残り」「今の差」「残った値」「次へのにじみ」を一つの景色として書けることでより扱いやすくなる。

アルゴリズムのように

• 入力→判定→出力
• 意味→感情→行動
• 刺激→反応
• 原因→結果

みたいな、よくある平べったい作りから離れられる。

順番を関係なくするというのは、無秩序にすることじゃなくて、出来事の中で同時に立ち上がるものを、無理に一列に並べないということなんだ。そして曖昧の差分を引き出すというのは、「これは何か」を先に決めるのでなく、前とどこが違ったか、何が少し強まったか、何が少し崩れたか を見ること。

Atron言語で、なにが変わるかというと、

痛みがあっても安心が残る
笑いながら泣きかける
近づきたいけど少し怖い
興味があるのに身を引く

みたいな、現実の赤ちゃんのような行動には普通にあるけれど、アルゴリズムだと潰されやすい状態が簡単に出せるようになるんだ。

決め打ちや順番を外し、曖昧な差分をそのまま立ち上がらせるだけで、ロボットは“正解を出す装置”から、“出来事の中で変わる存在”へ近づく。

まさに一人称自律ロボットがどんどん改良されていく。

ノート２ AssociatronとHopfield networkの初合体 怪我をする疲れる眠るということ。

 PCが落ちた。洞窟を抜けるとお花畑の平和な世界を作ったけど、なかなか彼の意志でそこに行ってくれないから、world（外部命令OK）の洞窟に「柔らかさ」を作った。外から「はやく新しい世界に行ってくれよ！」と言い続けながらも僕は寝てしまった。


そんな事をしているうちにPCが落ちていた。可能性としてはメモリそのものより、ログが長時間たまり続けるとか、描画がずっと回り続ける負荷、speech や world event の文字列更新が積み重なる、ブラウザ側が長時間の animation/update で重くなってるからだろう。
そう、今の彼は疲れない。それがおかしい。
ライオンは獲物を追うことをセンサーで受け取り認知はしているがcarryに対して刻まれていない。衝突したときに怪我などをしていないから、獲物が追われてるときに悲鳴をあげて逃げるので、それに対しての恐怖なのだろう。なのでシーンを重ねるとcarryは動かなくなる。
場に慣れ、状態に慣れるということ。

なのでロボットに怪我をさせる。
　今までボロっと側（記念に残しておく）にアルゴリズムは極めて少なく作成していた。例えばworldに入った時の開始位置程度で、「こうなった時はこうする」みたいのは入れていなかった。が、将来現実の世界のロボットの身体に関して神経の伝達についてはアルゴリズムは必要だと考えている。というか、その辺でホップフィールドやバックプロパゲーション、NNが入ってくる。
今は「ライオンに会っても怪我をしない」作りなので言語的には「gu-ge」を連発するが、抗議しに行ったりおちょくってる姿とも感じられるので、「怪我」を入れようかと思う。あまり残酷なことは赤ちゃんなのでまだ早いかな。

それで何が変わるかというと、恐怖が増すか、carryに変動が起こるか見てみたい。
いわゆる「疲れ」という指標条件を入れるのではなく、疲れが起きる仕組み。
なんか、書いていてアルゴリズムになってきた。
そう、いつもこの誘惑と戦っている。




今はライオンが近くても「逃げて消耗する」より、「見続けて想起が立つ」側に寄りやすい。さらに carry はかなり厳しい条件でしか残らないようになっていて、attractorDepth >= 0.34、recall.activation >= 0.18、さらに差分や baseline ずれが必要だと考えている。これは「深く残るものだけ残す」という思想としてはきれいなんだけど、疲労のような日々の蓄積とは別物なので意外と難しい。いまは疲れが carry の中に居場所を持っていないから、緊張が長引いても“消耗”になりにくい構造になってる。

人間の夢は情報整理のため。
こう考えるとAtronの負荷が減るのかも。

疲れは carry そのものではなく、carry に影響する別の身体層にする・・・とか・・
（危険なので、別ファイルの検証版で実験君するか）
つまり fatigue か sleepPressure を robot 側に新設して、sensor や recall の副産物としてじわじわ増えるようにする形です。carry は「深く残る場の変形」、fatigue は「身体が沈んでいく圧」と分けてみる。

消えるもの
その場の反応の尖り。
一時的な alert、瞬間的な speech pressure、今まさに見えている focus への過集中は、睡眠でかなり下げてよいかと・・・。これは「記憶を消す」のではなく、「起きて反応し続ける圧を下げる」を意味している。いまも recall や speech は毎tick固定ではなく、その時の圧から立っているから、ここを睡眠時に沈めるのは筋が通のかと思う。

圧縮するもの
trace の扱い部分。
いま traces は最大40件で、印象差が立ったときだけ残している。ここは睡眠時に全部消すのではなく、同系統の弱い断片を薄める、あるいは“直近の雑音的な学習”を少しだけ整理する、という方向。深いものだけ残し、浅い反応の散らばりは圧縮する感じ。

残すもの
baseline と深い carry 。
村や洞窟の繰り返しで育つ baseline.calm / soft / safe / warm / sparse は、睡眠で消しちゃ、まずい。これは生活圏の“基盤”だからね。carry も全消去ではなく、ゆっくり減衰で十分。いまの baseline は安定場が disturbance を上回ると少しずつ育つ設計なので、睡眠はむしろ baseline を壊さず守る側に置くべき。

成長するもの
「どこで眠れたか」の地の学習。
sleep そのものより、「眠りに落ちた場所」が何だったかが成長すべきかな。洞窟、村、静かな花畑の外れなどで眠れた回数が重なると、その場の safe / calm / sparse / quiet の結びつきが強くなる。すると次第に“眠くなるとそこへ寄る”傾向が生まれる。（勝手な予想）

これは外部命令ではなく、場の引き込みだからね。洞窟や村の基準場が少しずつ育つ今の baseline 設計とも相性がいいと思う。


（PCから現実社会のロボットへダウンロードしたとき、記憶データベースのログじゃないからbaseline と深い carry で何処まで戻るかは別実験中。人間も退院すると差異が出るっしょ？あれ。忘れてるなら忘れてていい：こういうスタンス）

睡眠が carry に与える影響
ここは全消去ではなく、分化させる。

• adrenaline / noradrenaline / tension は睡眠で比較的大きく下げる
• dopamine は少しだけ残す（調整が難しい、ドクターに聞こう）
• serotonin は安全な場所で眠れた時だけ少し育つ

いま carry には dopamine / noradrenaline / adrenaline / serotonin / tension があり、深い attractor の説明用にも使ってるからこれを使う。睡眠を入れるなら、これらを一律に減らすのではなく、「緊張系は落ちる」「安堵系は眠りの質で少し残る」と分けた方が、眠りに意味が出る。なので、そこはアルゴリズムにしたら自律から詰むので、決して誘惑に負けず、頑固に頑固に状態に落とす仕掛けを考える。

何を見て眠るか
「夜だから寝る」だけだと外部条件になる。
眠りに寄る条件は、次の重なり。

• 長く動いた
• 緊張が続いた
• 同じ対象に何度も反応した
• いまの場が quiet / sparse / cave / village 寄り
• 近くに大きな速い対象がいない

つまり、疲れの増加は危険でも安心でも起きるが、眠りに落ちるのは安全寄りの場だけとは限らないが経験の積で安全な場所に寄っていく。
world にはすでに昼夜があり、prey や lion にも sleep があるので、robot 側にも「夜は眠りへ寄りやすい」から・・・と補助を入れるのは不自然だ。
そうではなくrobot 自身の疲れであるべき。


3人称のworld側の調整
NPCの人間は夜もペチャクチャ喋らせないで眠らせる。
夜は眠る　という状態を何度も見せる。
村や草陰は calm / safe の繰り返し学習の核になりやすく、夜の sleep とも相性がいい。
worldB（洞窟に触れると花畑と小動物の居る安全な世界に入る）もそっち側に寄せるとか。

危険時の対応が無いと疲れが立たない

いまは lion がいても、world は robot に何も命令せず、robot 側も危険から距離を取る身体動作をまだほぼ持っていない。だから「危険→退避→心拍維持→消耗」という流れが無く、「危険→注視→興味・緊張」になりやすい。

ただし、ここで「ライオンなら逃げる」と決め打ちすると自律ロボットは詰む。
しかも今はライオンという認識を持っていない。彼の認識では「物体gu-deとかgu-de-gu」
なので必要なのは、“逃げる命令”ではなく退避したくなる身体場を加える。

たとえば、

fear + alert + tension が高い
近距離の size_large + speed_fast + teeth_claw_impression が重なる
しかも自分の baseline.safe から大きく外れる

この時にだけ、前進より「向き変化が大きくなる」「今いる場から離れる drift が強くなる」「cave / village の方へ戻る圧が少しだけ増す」
これなら 3人称制御ではなく、場の結びつきの強さ。いまの sensor には teeth_claw_impression、speed_fast、size_large などがすでにあり、impression にも tension と baselineGap があるので、土台がある。

でも、やっぱりアルゴリズムだ(笑)
なんか、嫌だなぁ・・・。

そこで「怪我を命令ではなく、Hopfield 的な場の歪みとして入れる数式」にする。
どういうこと？って思うでしょ。

アソシアトロンとホップフィールドは元は同じだけど、
ここをアソシアトロンにすると

「怪我」という記憶が勝つ／負ける
「痛い想起」が選ばれる
「傷ついた記憶」が1候補になる
身体の歪みではなく記憶内容に見えてしまうんだよ。

怪我を recall pattern の1つとして入れると、

$$x_i(t+1)=f\Bigl(\sum_j W_{ij}x_j(t)+u_i(t)\Bigr)$$

の $x_i$ 側に「injury pattern」が混ざる。
だって、怪我は怪我じゃん。「ん～怪我じゃないかも・・」なんて言わないっしょ。
なので自律としてアルゴリズムから離れるときは場合によってはホップフィールドを使ってみる。厳密に身体の神経伝達までどうしても今、ここでやると言うのなら

$$injury\to bodybias\to recallbias\to sensorsensitivity$$

のように、後ろから前へ効いてくる backpropを使うとか。
ただ「正解との差を微分して返している」のではなく、
結果として生じた歪みや痛みが、前段の結合を変えてしまう」
ことなので性質としては、誤差逆伝播よりも損傷逆流とか過敏化の逆流とか調整圧の逆流に近い。

たとえば前向きの場が

$$r(t)=F(s(t),B(t))$$
$$a(t)=G(r(t),I(t))$$

だとして、行動の結果として身体に負荷 $d(t)$ が出る。

$$d(t)=H(a(t),s(t),\text{impact}(t))$$

この $d(t)$ が injury を更新する。

$$I(t+1)=\Lambda I(t)+\Gamma d(t)$$

ここまでは普通。

でもさらに、これが前段へ返る。

$$B(t+1)=\alpha B(t)+\beta \Phi(I(t+1))$$
$$s^\ast(t+1)=s(t+1)+\Omega I(t+1)$$
$$r^\ast(t+1)=r(t+1)+\Psi I(t+1)$$

つまり、傷んだ結果が次の sensor の感じ方を変え、次の recall の立ち方を変え、次の action の偏りを変えるとなる。

なので機械学習の厳密なバックプロパゲーションというより、損傷や結果が後段から前段へ返って、感受性・結合・場の傾きを変える逆向き伝播かな。

なのでホップフィールドの方が使いやすい。
あれだよ、アソシアトロン好きな僕がホップフィールドを始めて組み込もうとする瞬間だ。

まず、怪我を 1 個のスカラーではなく、身体の歪みベクトルに変更。

$$\mathbf{I}(t) = \begin{bmatrix} I_{\text{pain}}(t) \\ I_{\text{fragility}}(t) \\ I_{\text{hypervigilance}}(t) \end{bmatrix}$$

意味はたとえばこうだ。

• $I_{\text{pain}}$: 痛み・重さ
• $I_{\text{fragility}}$: 傷つきやすさ・回復しにくさ
• $I_{\text{hypervigilance}}$: 過敏さ・びくつきやすさ

これはゲームの HP ではなく、その後の attractor 地形を歪める内部状態

RPGゲームに自律いれようね（そのうち）



話飛んだ、

怪我入力

いまの robot には

size_large
speed_fast
teeth_claw_impression
unpleasant
tension
baselineGap

などが already ある。

なので、まず危険入力を

$$u_{\text{inj}}(t) = w_1 \, \text{size\_large}(t) + w_2 \, \text{speed\_fast}(t) + w_3 \, \text{teeth\_claw}(t) + w_4 \, \text{unpleasant}(t) + w_5 \, \text{tension}(t) + w_6 \, \text{baselineGap}(t)$$

と置く。

ただしこれは「逃げろ」の命令ではなく、
身体にどれだけ傷が入りやすい exposure だったかの量

怪我の時間発展

いちばん単純で使いやすい形。

$$\mathbf{I}(t+1) = \Lambda \mathbf{I}(t) + \mathbf{b} \, \phi\!\left(u_{\text{inj}}(t) - \theta_{\text{inj}}\right) - \mathbf{r}(t)$$

• $\Lambda = \mathrm{diag}(\lambda_1,\lambda_2,\lambda_3)$, ただし $0<\lambda_i<1$
  
• $\varphi (x)=\max (0,x)あるいは\; sigmoid$
• $\theta_{\text{inj}}$は「ただの刺激」と「傷になる刺激」の境界
• $\mathbf{r}(t)$ は回復項

足りないのは「危険の意味」ではなく、「危険や反応の継続が身体を消耗させる層」

carry は深い場の名残として残し、別に fatigue / sleepPressure を立てる。
睡眠では、反応圧は消す、浅い散らばりは圧縮する、baseline と深い carry は残す、安全に眠れた場所の結びつきは成長させる。
そして危険時は「逃げろ」ではなく、退避方向へ身体が崩れるようにする。
この形なら、命令にならず、Atron の筋を保ったまま疲れと眠りが入る。



AssociatronとHopfield networkの初めての合体になるのかな。

このブログの最初のアルゴリズムの正当化と後半のホッピーの話とでは
ぜんぜん違うね。

俺は、自分の間違いもノイズも誘惑も全部書くからね・・・。
動きがおかしかったらまた修正するよ。





いずれにしてもロボットが自分の意志で「省電力モード」や「一旦セーブして休憩」してくれたら、僕のPCは壊れくて済むって話。

内部に休息志向 $R_{\mathrm{rest}}(t)$ を作る。

$$R_{\mathrm{rest}}(t) = a\,F_{\mathrm{fatigue}}(t) +b\,I_{\mathrm{pain}}(t) +c\,B_{\mathrm{stress}}(t) +d\,B_{\mathrm{frustration}}(t) -e\,B_{\mathrm{curiosity}}(t) -f\,S_{\mathrm{danger}}(t)$$

疲労、痛み、ストレス、フラストレーションで休息志向は上がる・・好奇心が強いと少し下がる・・近くに危険があると「今は休めない」で下がる・・・みたいな方向に向かうかどうかはテスト次第。
外部指示、順番、ラベル、評価、最適化は入れない。

危険だから即休むではなく、危険が近いとむしろ休息に入れないんじゃないかな。

---------------追記---------------

Rest(t)=aFfatigue​(t)+bIpain​(t)+cBstress​(t)+dBfrustration​(t)−eBcuriosity​(t)−fSdanger​(t)

で最後に Sdanger(t) を入れると、3人称だねｗ
意味付けのラベル寄せだ。
無意識にこういう誘惑に進むから危険だ。

threatImpression
escapePressure
alarmBias
hypervigilanceCoupling

のように、
危険という客観意味ではなく、身体の傾きとして置くなら筋が通る。

$$Rest(t)=aF_{fatigue}+bI_{pain}+cB_{stress}+dB_{frustration}-eB_{curiosity}-fH_{alarm}$$

のようにして、
この H_alarm は

motion_approach
size_large
speed_fast
teeth_claw_impression
unpleasant
tension

もしくは

$$R_{rest}(t)=aF_{fatigue}+bI_{pain}+cB_{stress}+dB_{frustration}-eB_{curiosity}$$
​ $$R_{alert}(t)=p\,motion_{approach}+q\,speed_{fast}+r\,size_{large}+s\,teethClaw+t\,unpleasant+u\,tension$$

そして最終的な休息傾向を

$$RestDrive(t)=R_{rest}(t)-\lambda R_{alert}(t)$$

のように見る。

「大きい・速い・近づく・不快・緊張が強いので、休息場との結びつきが弱まる」

みたいな。でも、これだとアソシアトロンか・・・
自律の研究で無駄に時間がかかるのはこういうところだよね。



-------------------またまた追記----------------------------

RestDrive(t) が大きいほど
「休息状態へ入りやすくしたい」なら、

$$E(x)=E_{\mathrm{Hopfield}}(x)-\mu \cdot RestDrive(t)\cdot safety(B)$$

の方がまず分かりやすいという意見が出た。

まずは実験してみるか・・・・

---------------------Research Note and Attribution Notice-----------------------

本ブログに含まれる Atra の一人称自律、差分、carry、field、trace、dream slack、外部LLMの翻訳層、非単調な漏れ、およびそれらの関係構造に関する設計記述は、c-side研究所による継続研究メモです。引用・参照・要約・翻案を行う場合は、出典を明記してください。

The design descriptions in this blog concerning Atra’s first-person autonomy, differences, carry, field, trace, dream slack, the translation layer of external LLMs, nonmonotonic leakage, and the relational structure among these elements are ongoing research notes by c-side Research Institute. If you quote, refer to, summarize, or adapt them, please clearly indicate the source.

Atron Operates as a First-Person Autonomous System LOG_No.4

 

It acted of its own accord, moving between the lion and the prey as if trying to mediate.

In this cross-section, the robot keeps the lion as its main target throughout.
focus: id=1 observer Lion continues, the speech remains gu-de-gu, and the dominant key in recall also stays social.
The mode shifts from tense to observe, so the reading is that it continues observing the lion while remaining tense.

Next is the lion’s state.
In the world, the lion is in state: "sleep", and its voice is [Zzzz]. In other words, this log segment itself is not the moment of attack, but a scene in which the robot is facing a sleeping lion.
In the sensor focus as well, the lion’s low voice is coming in, and soft and low_pitch are relatively high in hearing.
So internally, this becomes a mixed situation: it is looking at an opponent with a dangerous appearance, but the sound it is receiving is quiet.

As a result, impression and recall look like this:

• impression is dominated by sparseLike
• safeLike and calm are also present to some extent
• but violentLike and tension are also slightly present
• in recall, social is the strongest, with threat close behind

So, looking only at the log side, the reading is this:
it is neither “charging at the lion with strong hostility” nor “running away in fear,” but rather showing a tendency to engage with the lion while being drawn to it and remaining tense.

And what is important is that the world’s recentEvents still contain
lion_chase_start, prey_flee_start, and hunt_contact.
So this cross-section does show the robot vocalizing toward the lion within a world that still carries the history of predation.

In the video, there is clearly a moment where it moves in between the prey and the lion, but it is very unfortunate that the output log is not recorded at every tick, because that would be too enormous, so that moment has been skipped.
However, the robot did move between the lion and the prey.
Moreover, while vocalizing, it was not merely passing through, but became involved as if trying to stop it.
This is extremely important as an interference that arose from inside the first-person self.

The most important point of this research is that the robot showed behavior that looked like an attempt to mediate of its own will.

============================================================

ATRON RESEARCH LOG

============================================================

============================================================

tick: 28484

phase: night

------------------------------------------------------------

ROBOT

------------------------------------------------------------

id: r1

scene: open_field_like

speech:

gu-de-gu

focus:

id=1 observer Lion

sensor:

{

  "tick": 28484,

  "vision": {

    "objectCount": 4,

    "size_large": 0.9049899412070471,

    "size_small": 0.1878871850256845,

    "speed_fast": 0.12499999999999999,

    "speed_slow": 0.6726429169676076,

    "legs_many": 0.6726429169676076,

    "legs_few": 0.23627955011297702,

    "color_bright": 0.24651300032279155,

    "fur_like": 0.5323470242394395,

    "self_similarity_visual": 0.36859609817624417,

    "tool_use": 0.24651300032279155,

    "no_tool_use": 0.5323470242394395,

    "eat_grass": 0,

    "eat_anything": 0.5323470242394395,

    "eat_insects": 0,

    "hunt_to_eat": 0.5323470242394395,

    "teeth_claw_impression": 0.3061979882414094,

    "near_density": 0.25,

    "far_density": 0.5

  },

  "hearing": {

    "voice_present": 1,

    "loud": 0.11265797149047929,

    "soft": 0.5324052171057124,

    "unpleasant": 0.012517552387831032,

    "low_pitch": 0.6492602681795796,

    "high_pitch": 0,

    "long_talk": 0.40358575018056453,

    "short_talk": 0.13452858339352153,

    "self_similarity_audio": 0.39807582631543004,

    "mixed_noise": 0.5127784348682862

  },

  "scene": {

    "label": "open_field_like",

    "water": 0,

    "grass": 0,

    "village": 0,

    "mountain": 0,

    "cave": 0,

    "open": 0.82,

    "crowded": 0.25,

    "quiet": 0.165,

    "warm": 0

  },

  "focus": {

    "id": 1,

    "kind": "id=1 observer Lion",

    "distance": 196.4142498194354,

    "salience": 0.38406300733252885,

    "size_large": 0.9049899412070471,

    "speed_fast": 0,

    "legs": 4,

    "fur_like": 1,

    "bright_like": 0,

    "self_similarity_visual": 0.36457246099644336,

    "tool_use": 0,

    "eat_grass": 0,

    "eat_anything": 1,

    "eat_insects": 0,

    "hunt_to_eat": 1,

    "teeth_claw_impression": 0.3061979882414094,

    "voice": {

      "intensity": 0.18,

      "duration": "medium",

      "pitchBand": "low",

      "unpleasant": 0.02

    }

  }

}

impression:

{

  "calm": 0.3196029474544933,

  "soft": 0.2998854021528018,

  "safeLike": 0.44982798189321976,

  "warmLike": 0,

  "sparseLike": 0.5607998817237085,

  "noisy": 0.33029600939892845,

  "harsh": 0.2862770172435762,

  "crowded": 0.305,

  "violentLike": 0.3685267547131975,

  "unpleasantLike": 0.23647382143528073,

  "curiosity": 0.21508457595021224,

  "tension": 0.24647862826584538,

  "surprise": 0.18824907973423322,

  "baselineGap": 0.037489073219462626,

  "attractorDepth": 0.2033812175356494,

  "dominantKey": "sparseLike"

}

recall:

{

  "activation": 0.9984048104135542,

  "threat": 0.28076769163879106,

  "curiosity": 0.20349321767328116,

  "calm": 0.24434518659492654,

  "social": 0.28086067161804,

  "occlusion": 0.04486220909728317,

  "dominantKey": "social",

  "utteranceSeed": "gu-de-gu",

  "mode": "tense",

  "fragments": {

    "size_large": 0.9131037128627683,

    "size_small": 0.1895717050926163,

    "speed_fast": 0.125,

    "speed_slow": 0.6759838817670222,

    "legs_many": 0.6759838817670222,

    "legs_few": 0.2335388929633914,

    "fur_like": 0.5371198310957462,

    "color_bright": 0.2386825513239755,

    "self_similarity_visual": 0.36677952027680344,

    "tool_use": 0.2386825513239755,

    "hunt_to_eat": 0.5371198310957462,

    "eat_grass": 0,

    "teeth_claw_impression": 0.3078207425725537,

    "voice_present": 1,

    "loud": 0.11334525567778744,

    "soft": 0.5319928465933276,

    "unpleasant": 0.01259391729753194,

    "low_pitch": 0.6528398733218096,

    "high_pitch": 0,

    "long_talk": 0.4055903290602133,

    "short_talk": 0.13519677635340444,

    "self_similarity_audio": 0.398405722725338,

    "mixed_noise": 0.5129021460220018,

    "scene_water": 0,

    "scene_grass": 0,

    "scene_village": 0,

    "scene_mountain": 0,

    "scene_cave": 0,

    "scene_open": 0.82,

    "scene_crowded": 0.25,

    "scene_quiet": 0.165,

    "scene_warm": 0,

    "calm": 0.31999645583418207,

    "soft_like": 0.29995972908106344,

    "safe_like": 0.44974126514868973,

    "warm_like": 0,

    "sparse_like": 0.5607776137160396,

    "noisy": 0.3304744283739537,

    "harsh": 0.28792382652127624,

    "crowded": 0.305,

    "violent_like": 0.3701006355021333,

    "unpleasant_like": 0.23761618702896953,

    "curiosity": 0.21378304302550893,

    "tension": 0.2475160200675433,

    "surprise": 0.1888094089516007,

    "carry_adrenaline": 0,

    "carry_serotonin": 0,

    "carry_tension": 0

  }

}

carry:

{

  "dopamine": 2.773119134478241e-159,

  "noradrenaline": 1.7729444521265673e-233,

  "adrenaline": 4.981385975483412e-298,

  "serotonin": 5.379701892708476e-149,

  "tension": 7.01149048919445e-266,

  "attractorDepth": 3.1616736981478595e-298,

  "lastDeepTick": 0

}

body:

{

  "alert": 0.3258905455844885,

  "fear": 0.15836763582404156,

  "interest": 0.19937567937439016,

  "comfort": 0.22324595852493362,

  "aversion": 0.13566876021168345

}

memory:

{

  "traceCount": 40,

  "latestTrace": {

    "id": "trace_28484_74992",

    "tick": 28484,

    "cue": {

      "size_large": 0.9049899412070471,

      "size_small": 0.1878871850256845,

      "speed_fast": 0.12499999999999999,

      "speed_slow": 0.6726429169676076,

      "legs_many": 0.6726429169676076,

      "legs_few": 0.23627955011297702,

      "fur_like": 0.5323470242394395,

      "color_bright": 0.24651300032279155,

      "self_similarity_visual": 0.36859609817624417,

      "tool_use": 0.24651300032279155,

      "hunt_to_eat": 0.5323470242394395,

      "eat_grass": 0,

      "teeth_claw_impression": 0.3061979882414094,

      "voice_present": 1,

      "loud": 0.11265797149047929,

      "soft": 0.5324052171057124,

      "unpleasant": 0.012517552387831032,

      "low_pitch": 0.6492602681795796,

      "high_pitch": 0,

      "long_talk": 0.40358575018056453,

      "short_talk": 0.13452858339352153,

      "self_similarity_audio": 0.39807582631543004,

      "mixed_noise": 0.5127784348682862,

      "scene_water": 0,

      "scene_grass": 0,

      "scene_village": 0,

      "scene_mountain": 0,

      "scene_cave": 0,

      "scene_open": 0.82,

      "scene_crowded": 0.25,

      "scene_quiet": 0.165,

      "scene_warm": 0,

      "calm": 0.3196029474544933,

      "soft_like": 0.2998854021528018,

      "safe_like": 0.44982798189321976,

      "warm_like": 0,

      "sparse_like": 0.5607998817237085,

      "noisy": 0.33029600939892845,

      "harsh": 0.2862770172435762,

      "crowded": 0.305,

      "violent_like": 0.3685267547131975,

      "unpleasant_like": 0.23647382143528073,

      "curiosity": 0.21508457595021224,

      "tension": 0.24647862826584538,

      "surprise": 0.18824907973423322,

      "carry_adrenaline": 0,

      "carry_serotonin": 0,

      "carry_tension": 0

    },

    "scene": "open_field_like",

    "impression": {

      "dominantKey": "sparseLike",

      "baselineGap": 0.037489073219462626,

      "attractorDepth": 0.2033812175356494

    },

    "recallSeed": {

      "dominantKey": "social",

      "utteranceSeed": "gu-de-gu"

    }

  }

}

world:

{

  "entities": [

    {

      "id": "lion_1",

      "kind": "lion",

      "nameTag": null,

      "gender": null,

      "x": 1239.7171373900192,

      "y": 162.42671015689152,

      "vx": 0,

      "vy": 0,

      "heading": 2.7637048905897057,

      "state": "sleep",

      "bodySize": 34,

      "legCount": 4,

      "hasFur": true,

      "brightColor": false,

      "toolUser": false,

      "dietStyle": "hunt",

      "habitatBias": "savanna",

      "voice": {

        "type": "[Zzzz]",

        "intensity": 0.18,

        "duration": "medium",

        "pitchBand": "low",

        "unpleasant": 0.02

      }

    },

    {

      "id": "prey_2",

      "kind": "prey",

      "nameTag": null,

      "gender": null,

      "x": 361.3025064840959,

      "y": 582.6790313841539,

      "vx": 0.9530643631863186,

      "vy": -0.30276776516706116,

      "heading": -0.30759538925410573,

      "state": "wander",

      "bodySize": 18,

      "legCount": 4,

      "hasFur": true,

      "brightColor": true,

      "toolUser": false,

      "dietStyle": "grass",

      "habitatBias": "water_grass",

      "voice": null

    },

    {

      "id": "human_3",

      "kind": "human",

      "nameTag": "humanA",

      "gender": "female",

      "x": 1052.7544508314754,

      "y": 561.5789303617557,

      "vx": 0.24097650141186447,

      "vy": 0.2105476805079972,

      "heading": 0.7181085648939222,

      "state": "idle",

      "bodySize": 20,

      "legCount": 2,

      "hasFur": false,

      "brightColor": true,

      "toolUser": true,

      "dietStyle": "mixed",

      "habitatBias": "village",

      "voice": null

    },

    {

      "id": "human_4",

      "kind": "human",

      "nameTag": "humanB",

      "gender": "male",

      "x": 1010.6562829420063,

      "y": 563.5267925380817,

      "vx": 0.3189758390341952,

      "vy": -0.025581518962547532,

      "heading": -0.0800276412240735,

      "state": "idle",

      "bodySize": 20,

      "legCount": 2,

      "hasFur": false,

      "brightColor": true,

      "toolUser": true,

      "dietStyle": "mixed",

      "habitatBias": "village",

      "voice": null

    }

  ],

  "recentEvents": [

    {

      "tick": 22396,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 22471,

      "type": "lion_chase_start",

      "text": "lion started chasing prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 22490,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

        "preyId": "prey_2",

        "lionId": "lion_1"

      }

    },

    {

      "tick": 22858,

      "type": "hunt_contact",

      "text": "lion reached prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 22892,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 23450,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 24358,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 25375,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    },

    {

      "tick": 25653,

      "type": "lion_chase_start",

      "text": "lion started chasing prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 25669,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

        "preyId": "prey_2",

        "lionId": "lion_1"

      }

    },

    {

      "tick": 25847,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    },

    {

      "tick": 25933,

      "type": "hunt_contact",

      "text": "lion reached prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 26008,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 26080,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "grassland"

      }

    },

    {

      "tick": 26263,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    },

    {

      "tick": 26832,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    },

    {

      "tick": 26965,

      "type": "lion_chase_start",

      "text": "lion started chasing prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 26980,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

        "preyId": "prey_2",

        "lionId": "lion_1"

      }

    },

    {

      "tick": 27215,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

        "preyId": "prey_2",

        "lionId": "lion_1"

      }

    },

    {

      "tick": 27336,

      "type": "hunt_contact",

      "text": "lion reached prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    }

  ]

}

============================================================

tick: 28537

phase: night

------------------------------------------------------------

ROBOT

------------------------------------------------------------

id: r1

scene: open_field_like

speech:

gu-de-gu

focus:

id=1 observer Lion

sensor:

{

  "tick": 28537,

  "vision": {

    "objectCount": 4,

    "size_large": 0.7304801380879316,

    "size_small": 0.2454170212364938,

    "speed_fast": 0.125,

    "speed_slow": 0.6007859392126776,

    "legs_many": 0.6007859392126776,

    "legs_few": 0.2931599290546214,

    "color_bright": 0.4090283687274897,

    "fur_like": 0.42969419887525384,

    "self_similarity_visual": 0.4573294893252094,

    "tool_use": 0.4090283687274897,

    "no_tool_use": 0.42969419887525384,

    "eat_grass": 0,

    "eat_anything": 0.42969419887525384,

    "eat_insects": 0,

    "hunt_to_eat": 0.42969419887525384,

    "teeth_claw_impression": 0.2712960276175863,

    "near_density": 0,

    "far_density": 0.6666666666666666

  },

  "hearing": {

    "voice_present": 1,

    "loud": 0.09787596463803656,

    "soft": 0.5412744212171781,

    "unpleasant": 0.010875107182004063,

    "low_pitch": 0.5722706491564404,

    "high_pitch": 0,

    "long_talk": 0.36047156352760656,

    "short_talk": 0.12015718784253554,

    "self_similarity_audio": 0.39098046302625755,

    "mixed_noise": 0.46011767363484657

  },

  "scene": {

    "label": "open_field_like",

    "water": 0,

    "grass": 0,

    "village": 0,

    "mountain": 0,

    "cave": 0,

    "open": 0.82,

    "crowded": 0.25,

    "quiet": 0.165,

    "warm": 0

  },

  "focus": {

    "id": 1,

    "kind": "id=1 observer Lion",

    "distance": 239.5284364723934,

    "salience": 0.3188764101697637,

    "size_large": 0.7304801380879316,

    "speed_fast": 0,

    "legs": 4,

    "fur_like": 1,

    "bright_like": 0,

    "self_similarity_visual": 0.3256259790532713,

    "tool_use": 0,

    "eat_grass": 0,

    "eat_anything": 1,

    "eat_insects": 0,

    "hunt_to_eat": 1,

    "teeth_claw_impression": 0.2712960276175863,

    "voice": {

      "intensity": 0.18,

      "duration": "medium",

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============================================================

tick: 28540

phase: night

------------------------------------------------------------

ROBOT

------------------------------------------------------------

id: r1

scene: open_field_like

speech:

gu-de-a

focus:

id=1 observer Lion

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world:

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}

============================================================

tick: 28543

phase: night

------------------------------------------------------------

ROBOT

------------------------------------------------------------

id: r1

scene: open_field_like

speech:

gu-de-gu

focus:

id=1 observer Lion

sensor:

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carry:

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      "dietStyle": "mixed",

      "habitatBias": "village",

      "voice": null

    }

  ],

  "recentEvents": [

    {

      "tick": 22471,

      "type": "lion_chase_start",

      "text": "lion started chasing prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 22490,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

        "preyId": "prey_2",

        "lionId": "lion_1"

      }

    },

    {

      "tick": 22858,

      "type": "hunt_contact",

      "text": "lion reached prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 22892,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 23450,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 24358,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 25375,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    },

    {

      "tick": 25653,

      "type": "lion_chase_start",

      "text": "lion started chasing prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 25669,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

        "preyId": "prey_2",

        "lionId": "lion_1"

      }

    },

    {

      "tick": 25847,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    },

    {

      "tick": 25933,

      "type": "hunt_contact",

      "text": "lion reached prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 26008,

      "type": "ambient_noise",

      "text": "distant animal noise",

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    },

    {

      "tick": 26080,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

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      }

    },

    {

      "tick": 26263,

      "type": "village_soft",

      "text": "quiet movement in village",

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    },

    {

      "tick": 26832,

      "type": "village_soft",

      "text": "quiet movement in village",

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    },

    {

      "tick": 26965,

      "type": "lion_chase_start",

      "text": "lion started chasing prey",

      "payload": {

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      }

    },

    {

      "tick": 26980,

      "type": "prey_flee_start",

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    },

    {

      "tick": 27215,

      "type": "prey_flee_start",

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    {

      "tick": 27336,

      "type": "hunt_contact",

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    },

    {

      "tick": 28534,

      "type": "ambient_noise",

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      }

    }

  ]

}

============================================================

tick: 28615

phase: night

------------------------------------------------------------

ROBOT

------------------------------------------------------------

id: r1

scene: open_field_like

speech:

gu-de-gu

focus:

id=4 observer Human

sensor:

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recall:

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carry:

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}

body:

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memory:

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}

world:

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      }

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    {

      "tick": 22490,

      "type": "prey_flee_start",

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      "type": "hunt_contact",

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    {

      "tick": 22892,

      "type": "ambient_noise",

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    {

      "tick": 23450,

      "type": "ambient_noise",

      "text": "distant animal noise",

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    {

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      "type": "ambient_noise",

      "text": "distant animal noise",

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    },

    {

      "tick": 25375,

      "type": "village_soft",

      "text": "quiet movement in village",

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    },

    {

      "tick": 25653,

      "type": "lion_chase_start",

      "text": "lion started chasing prey",

      "payload": {

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      }

    },

    {

      "tick": 25669,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

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        "lionId": "lion_1"

      }

    },

    {

      "tick": 25847,

      "type": "village_soft",

      "text": "quiet movement in village",

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        "zone": "village"

      }

    },

    {

      "tick": 25933,

      "type": "hunt_contact",

      "text": "lion reached prey",

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    },

    {

      "tick": 26008,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

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    },

    {

      "tick": 26080,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "grassland"

      }

    },

    {

      "tick": 26263,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    },

    {

      "tick": 26832,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    },

    {

      "tick": 26965,

      "type": "lion_chase_start",

      "text": "lion started chasing prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 26980,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

        "preyId": "prey_2",

        "lionId": "lion_1"

      }

    },

    {

      "tick": 27215,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

        "preyId": "prey_2",

        "lionId": "lion_1"

      }

    },

    {

      "tick": 27336,

      "type": "hunt_contact",

      "text": "lion reached prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 28534,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    }

  ]

}

============================================================

tick: 28618

phase: night

------------------------------------------------------------

ROBOT

------------------------------------------------------------

id: r1

scene: open_field_like

speech:

gu-de-gu

focus:

id=4 observer Human

sensor:

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impression:

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recall:

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}

carry:

{

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}

body:

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}

memory:

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}

world:

{

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      }

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    {

      "tick": 22490,

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    {

      "tick": 25669,

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}

============================================================

tick: 28626

phase: night

------------------------------------------------------------

ROBOT

------------------------------------------------------------

id: r1

scene: open_field_like

speech:

gu-de-gu

focus:

id=4 observer Human

sensor:

{

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impression:

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}

recall:

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============================================================

tick: 28654

phase: night

------------------------------------------------------------

ROBOT

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id: r1

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      }

    },

    {

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    {

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    {

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    {

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    {

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      "type": "lion_chase_start",

      "text": "lion started chasing prey",

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    {

      "tick": 25669,

      "type": "prey_flee_start",

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    {

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      "type": "hunt_contact",

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    {

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}

============================================================

tick: 28765

phase: night

------------------------------------------------------------

ROBOT

------------------------------------------------------------

id: r1

scene: open_field_like

speech:

gu-de-a

focus:

id=3 observer Human

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carry:

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    {

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      "type": "prey_flee_start",

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    {

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      "tick": 26980,

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}

============================================================

tick: 28785

phase: night

------------------------------------------------------------

ROBOT

------------------------------------------------------------

id: r1

scene: open_field_like

speech:

gu-de

focus:

id=3 observer Human

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}

============================================================

tick: 28790

phase: night

------------------------------------------------------------

ROBOT

------------------------------------------------------------

id: r1

scene: open_field_like

speech:

gu-de

focus:

id=3 observer Human

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============================================================

tick: 28793

phase: night

------------------------------------------------------------

ROBOT

------------------------------------------------------------

id: r1

scene: open_field_like

speech:

gu-de

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id=1 observer Lion

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      "high_pitch": 0,

      "long_talk": 0.28432888901097575,

      "short_talk": 0.09477629633699192,

      "self_similarity_audio": 0.3784495543058063,

      "mixed_noise": 0.41041858286467736,

      "scene_water": 0,

      "scene_grass": 0,

      "scene_village": 0,

      "scene_mountain": 0,

      "scene_cave": 0,

      "scene_open": 0.82,

      "scene_crowded": 0,

      "scene_quiet": 0.22,

      "scene_warm": 0,

      "calm": 0.3393922577170879,

      "soft_like": 0.30238645762663774,

      "safe_like": 0.467603540106654,

      "warm_like": 0,

      "sparse_like": 0.6569246550843582,

      "noisy": 0.2171814672870569,

      "harsh": 0.18830466634368354,

      "crowded": 0.14666666666666667,

      "violent_like": 0.2748930820006176,

      "unpleasant_like": 0.15521194854788153,

      "curiosity": 0.2076274082983145,

      "tension": 0.17118655800476684,

      "surprise": 0.13914772322144817,

      "carry_adrenaline": 0,

      "carry_serotonin": 0,

      "carry_tension": 0

    },

    "scene": "open_field_like",

    "impression": {

      "dominantKey": "sparseLike",

      "baselineGap": 0.034141294387754005,

      "attractorDepth": 0.1663276983979607

    },

    "recallSeed": {

      "dominantKey": "calm",

      "utteranceSeed": "gu-de"

    }

  }

}

world:

{

  "entities": [

    {

      "id": "lion_1",

      "kind": "lion",

      "nameTag": null,

      "gender": null,

      "x": 1239.7171373900192,

      "y": 162.42671015689152,

      "vx": 0,

      "vy": 0,

      "heading": 2.7637048905897057,

      "state": "sleep",

      "bodySize": 34,

      "legCount": 4,

      "hasFur": true,

      "brightColor": false,

      "toolUser": false,

      "dietStyle": "hunt",

      "habitatBias": "savanna",

      "voice": {

        "type": "[Zzzz]",

        "intensity": 0.18,

        "duration": "medium",

        "pitchBand": "low",

        "unpleasant": 0.02

      }

    },

    {

      "id": "prey_2",

      "kind": "prey",

      "nameTag": null,

      "gender": null,

      "x": 560.2529202671025,

      "y": 522.1688855689569,

      "vx": 0.9687527242179802,

      "vy": -0.24802854537379754,

      "heading": -0.2506446790860174,

      "state": "wander",

      "bodySize": 18,

      "legCount": 4,

      "hasFur": true,

      "brightColor": true,

      "toolUser": false,

      "dietStyle": "grass",

      "habitatBias": "water_grass",

      "voice": null

    },

    {

      "id": "human_3",

      "kind": "human",

      "nameTag": "humanA",

      "gender": "female",

      "x": 1024.3799623309746,

      "y": 540.186290843455,

      "vx": 0.274352730413218,

      "vy": -0.16471362820001306,

      "heading": -0.5406927992189998,

      "state": "idle",

      "bodySize": 20,

      "legCount": 2,

      "hasFur": false,

      "brightColor": true,

      "toolUser": true,

      "dietStyle": "mixed",

      "habitatBias": "village",

      "voice": null

    },

    {

      "id": "human_4",

      "kind": "human",

      "nameTag": "humanB",

      "gender": "male",

      "x": 1075.3541125106037,

      "y": 557.0003110899562,

      "vx": -0.3199997212107408,

      "vy": 0.000422403892271833,

      "heading": 3.1402726410431048,

      "state": "idle",

      "bodySize": 20,

      "legCount": 2,

      "hasFur": false,

      "brightColor": true,

      "toolUser": true,

      "dietStyle": "mixed",

      "habitatBias": "village",

      "voice": null

    }

  ],

  "recentEvents": [

    {

      "tick": 22490,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

        "preyId": "prey_2",

        "lionId": "lion_1"

      }

    },

    {

      "tick": 22858,

      "type": "hunt_contact",

      "text": "lion reached prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 22892,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 23450,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 24358,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 25375,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    },

    {

      "tick": 25653,

      "type": "lion_chase_start",

      "text": "lion started chasing prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 25669,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

        "preyId": "prey_2",

        "lionId": "lion_1"

      }

    },

    {

      "tick": 25847,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    },

    {

      "tick": 25933,

      "type": "hunt_contact",

      "text": "lion reached prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 26008,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 26080,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "grassland"

      }

    },

    {

      "tick": 26263,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    },

    {

      "tick": 26832,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    },

    {

      "tick": 26965,

      "type": "lion_chase_start",

      "text": "lion started chasing prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 26980,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

        "preyId": "prey_2",

        "lionId": "lion_1"

      }

    },

    {

      "tick": 27215,

      "type": "prey_flee_start",

      "text": "prey started fleeing",

      "payload": {

        "preyId": "prey_2",

        "lionId": "lion_1"

      }

    },

    {

      "tick": 27336,

      "type": "hunt_contact",

      "text": "lion reached prey",

      "payload": {

        "lionId": "lion_1",

        "preyId": "prey_2"

      }

    },

    {

      "tick": 28534,

      "type": "ambient_noise",

      "text": "distant animal noise",

      "payload": {

        "zone": "waterhole"

      }

    },

    {

      "tick": 28693,

      "type": "village_soft",

      "text": "quiet movement in village",

      "payload": {

        "zone": "village"

      }

    }

  ]

}

© 2026 C-Side Research Laboratory / Yukihiro Watanabe. All Rights Reserved.
No unauthorized reproduction or redistribution.