Physical AI フロンティア・レーダー · 2026-09-19

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フィジカルAIの最前線を毎日更新：8つの軸、三言語の要約、追跡可能な根拠。

lane distribution

本日の注目

1. GeoAAC: Geometry-Based Adaptive Action Chunking from Denoising Trajectories in VLA Policies

Action chunking is widely used for action generation and execution in Vision-Language-Action (VLA) policies, yet existing approaches commonly use a fixed action horizon.

2. rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inference

Factory work is a promising early scenario for embodied AI: assigning repetitive manual jobs to robots has clear economic payoff, and a structured station keeps the jobs tractable for current policies.

3. PASSAGE: Scaling Scene-Aligned Motion Learning for Perceptive Humanoid Traversal in Cluttered Environments

Humanoid robots can step over, squeeze past, and duck under obstacles, but learning to select and coordinate these behaviors from onboard perception remains challenging.

4. StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation

Hierarchical planning frameworks combine skills from multiple robot control policies for long-horizon task execution, where determining when to terminate the current skill and advance to the next subtask is essential.

5. Agile-WAM: An Agile Tactile World Action Model for Contact-Rich Robot Control

World Action Models (WAMs) advance beyond conventional visuomotor policies by jointly predicting future world states and robot actions, enabling the policy to learn physical dynamics that support effective control.

6. Robots Are Learning to Feel

Dexterous manipulation remains one of the biggest barriers keeping robots from successfully tackling a wide range of everyday tasks.

7. FIVE-VLA: Fast and EffectIVE Autonomous Driving with Recurrent Action Memory

State-of-the-art vision-language-action models (VLA) for autonomous driving face critical limitations: excessive parameter counts, inefficient high-resolution image processing, and lack of temporal memory.

8. Towards High-DoF Dexterous Manipulation through VLA Post-Training

Imitation-learned vision–language–action (VLA) foundation models acquire broad manipulation capabilities by scaling robot data across tasks and embodiments, but reliable deployment on a specific downstream task and hardware platform…

フロンティア基準（キュレーション）

1. RoboWorld: fast and reliable neural simulators for generalist robot policy evaluation

2. NVIDIA Jetson Thor: up to 2070 FP4 TFLOPS and 128 GB at 40-130 W

3. LeRobotDataset v3.0: a standardised, streamable format for robot learning data

4. π*0.6 / Recap: a VLA that learns from experience via advantage-conditioned policies

5. XPENG commissions its humanoid production lines; IRON walks off the line autonomously

6. The reliability gap: industrial buyers want 95-99% uptime; most humanoids run 30-90 minutes

軸ごとの分布

軸 #
基盤モデル（VLA / WAM） 8
データエンジン（収集 / 精査 / スケーリング） 6
学習と自己改善（RL / 事後学習） 3
シミュレーションと評価（sim2real / 閉ループ） 5
エッジとリアルタイム（オンデバイス推論 / 制御周期） 7
安全・権限・コンプライアンス 5
システムとオーケストレーション（長期タスク / 複数方策） 2
ハードウェアとサプライチェーン（機体 / コスト） 1

直近の推移

cadence

evidence mix

標準・規制ウォッチ

本リポジトリは技術観察であり、投資助言・製品保証・安全認証の結論ではありません。論文の主張は著者による自己報告です。

方法論 · アーカイブ