Kanata Suzuki

Working on AI and Robotics Research.

[早稲田大学 Waseda University @waseda_univ]

🤖脳の「予測する仕組み」をロボットへ🤖

理工学術院の三宅太文次席研究員、尾形哲也教授ら研究チームは、スマートロボットAIRECに新AIモデルScalable PV-RNNを実装💻

脳理論「予測処理」に基づき、異なる介護動作を同時学習💡次世代ロボットへの応用が期待されます🧑‍💻
https://t.co/WD7hArKQZO

[Kanata Suzuki @canacanata]

昨日は不定期の交流会でした。いつもより少し高いお店。

[Tetsuya Ogata / 尾形哲也 @tetsuyaogata1]

昨日は普段尾形研を助けてくれている博士メンバーで集まり。皆偉くなって本当にすごい。

[Kanata Suzuki @canacanata]

企業での研究開発に興味がある学生方に是非どうぞ。

[Kanata Suzuki @canacanata]

富士通の冬季インターン募集が出ていたようです（〆切8/18）。
https://t.co/xVNGFqtzOU

弊部署の募集テーマは「VLAのロバスト性、効率性、スケーラビリティ、および失敗回復に関する研究開発」で、私のチームでは主に模倣学習・強化学習を扱います。
（現在、夏季インターンで2名を受け入れ中です）

[Kanata Suzuki @canacanata]

Our paper is accepted to ICONIP2026!

K.Miyamoto, https://t.co/TroT2keN6M, T.Ogata, "How to Utilize Failure Demo Data?: Effective Data Selection for Imitation Learning Using Distribution Differences in Attention Mechanism"
https://t.co/19ycoIsdgt

Congratulations!

[Kanata Suzuki @canacanata]

TMLR has awarded this paper Survey Certificate!
https://t.co/5KOjoNpo5k

[Kanata Suzuki @canacanata]

Our paper is accepted to Transactions on Machine Learning Research (TMLR)!

S. A. Mehta, A. Hazarika, H. Zhang, F. Yang, R. Moriyama, W. Li, Y. Patel, K. Suzuki, "Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World Models"

Congratulations!

[Kanata Suzuki @canacanata]

Paper is now published:
https://t.co/DOmpTeC7dR

It is a comprehensive survey of representation learning, VLA, and world models. We presented a unified perspective on robot learning by organizing the existing methods along three complementary axes.

[Kanata Suzuki @canacanata]

Paper is now published:
https://t.co/DOmpTeC7dR

It is a comprehensive survey of representation learning, VLA, and world models. We presented a unified perspective on robot learning by organizing the existing methods along three complementary axes.

[Kanata Suzuki @canacanata]

Our paper is accepted to Transactions on Machine Learning Research (TMLR)!

S. A. Mehta, A. Hazarika, H. Zhang, F. Yang, R. Moriyama, W. Li, Y. Patel, K. Suzuki, "Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World Models"

Congratulations!

[Kanata Suzuki @canacanata]

This is joint research with Fujitsu Research of America and CMU.
Detailed paper information is coming soon.

[Kanata Suzuki @canacanata]

空いてたら尾形先生の模擬授業に潜ってみようかな

[Kanata Suzuki @canacanata]

そうか、今日は早大オープンキャンパスか。高校生がいっぱいいるなー

[Kanata Suzuki @canacanata]

来週は初めてMIRUに参加します(主著者の代理発表)。よろしくお願いします。

[Kanata Suzuki @canacanata]

発表とセッションチェア業務が終わったので、目的はだいたい完了した

[Kanata Suzuki @canacanata]

@hshige_86 @T_Miyake3 お二人ともありがとうございます！水族館が会場から近すぎるので、ひとまず行ってみます笑

[Kanata Suzuki @canacanata]

ジェノバ、遠いのを除けば今まで訪れた国際学会の場所で一番いいかもしれない

[Kanata Suzuki @canacanata]

ミラノ着きました。AIM@ジェノバまでけっこう距離あるのね

[Kanata Suzuki @canacanata]

いやーIROS厳しいっす、、

[Yuma Ichikawa @yuma_1_or]

▶️ Fujitsu Research Day in 東京大学を開催

我々が開発した「OneComp」や独自LLM「Photon」の紹介に加え, 世界と戦うためのAI研究のビジョンや日々の研究のリアルも共有します

日時: 2026年5月18日（月）17:00〜19:30
参加費：無料・食事あり
定員超過時：先着順の可能性あり
（専攻・学年不問）

[Eren Chen @ErenChenAI]

Why Robots work in Simulation but fail in Reality

One of the most frustrating moments in robotics:

Everything works perfectly in simulation. Then you deploy it on a real robot and suddenly:

The grasp misses
The arm shakes
The robot drifts
Contact becomes unstable
The motion looks correct, but the task still fails

How do you solve the sim to real problem?

At first, it sounds simple. Just move the code from simulation onto hardware.

But the gap between simulation and reality is much larger than most people think.

Simulation environments are extremely clean.

The table is flat.
Object geometry is accurate.
Friction is predefined.
Sensors are stable.
Robot joints behave exactly as expected.

But the real world is messy.

Lighting changes.
Depth sensors drift.
Objects reflect light differently.
Motors have delay.
Joints have backlash.
Contact forces behave unpredictably.
And robotics is a chain reaction.
A small perception error becomes a planning error.
The planning error becomes a control error.
The control error becomes an execution error.

Eventually, the robot misses the grasp by a few centimeters and the entire task fails.

And The hardest part is usually contact.

Humans think tasks like: grasping a cup, opening a door, inserting an object, pushing a box are trivial.

For robots, these are extremely difficult because contact is not clean physics. A tiny shift in friction, force, or surface geometry can completely change the result.

In simulation, objects are usually “well behaved.”
In reality:
objects slip
contact points shift
surfaces deform
collisions happen unexpectedly

This is why many robotic tasks fail not because the policy is fundamentally wrong, but because reality itself introduces uncertainty.

Sensors are also less reliable than people think.

The robot’s perception already contains error:
camera noise
unstable depth estimation
occlusion
pose estimation drift
changing lighting conditions

Sometimes the model itself is fine, but the input is already slightly wrong. By the time the error propagates to the end effector, the grasp fails.

The robot hardware itself is also imperfect.
Motors have latency.
Controllers have frequency limits.
Actuators have error.
Different loads change behavior.

In simulation, the robot follows commands perfectly.

In reality, it may move slightly slower, slightly off target, or slightly unstable. Those tiny differences are fatal in robotics because robots physically interact with the world.

Sim2Real being difficult does not mean simulation is useless. Simulation is still incredibly valuable: they are cheap, safe, scalable and reproducible.

A better way to think about simulation is: Simulation is the training ground, not the final battlefield.

Modern Sim2Real methods usually combine multiple approaches: making simulation more realistic, adding domain randomization, randomizing lighting, friction, object positions, and sensor noise, fine-tuning with real-world data.

The goal is not to make the robot adapt to one perfect virtual world. The goal is to make the robot robust enough to survive an imperfect real one.

The most important lesson in robotics is:

Success in simulation is only the first step. The real test begins when the robot touches the real world.

Video Credit: Kevin Zakka