Physical AI フロンティア・レーダー · 2026-10-05

言語：ZH · EN · 日本語

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。

本日の注目

1. Is Success All You Need? Investigating the Impact of Input Perturbations on VLA Behaviour in Tabletop Manipulation Tasks

Vision-Language-Action (VLA) models have achieved high task success rates on robot manipulation task benchmarks.

2. WBAG: A Whole-Body and Attached-Geometry Safety Framework for Vision-Language-Action Manipulation

Vision-language-action (VLA) policies have demonstrated impressive capabilities in generalizable robotic manipulation, but their deployment in the real world remains challenging due to potential collisions involving different parts of the…

3. NarrativeFlow: Flow-Based Vision-Language-Action Model Using Robot Velocity Fields

We focus on language-conditioned flow-based manipulation, where robot flows (robot velocity fields) serve as embodiment-agnostic, motion-centric representations for leveraging data collected from multiple robot platforms.

4. Divide-and-Remember: Recursive Action-Relevant Memory for Long-Horizon VLA Policies

Vision-language-action (VLA) models struggle on history-dependent manipulation tasks, where the current observation alone does not determine the action, and the policy needs a memory of the history.

5. PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots

Quadrupedal locomotion requires balancing conflicting objectives such as command tracking, stability, and energy efficiency, yet conventional reinforcement learning (RL) hardcodes these priorities into a fixed scalar reward at training…

6. TOAST: Stochastic Robot Action Tokenization for Autoregressive Vision-Language-Action Models

Autoregressive Vision-Language-Action models often represent continuous robot actions as discrete token sequences, enabling action prediction with standard next-token objectives.

7. UniWAM: Unified World-Action Model

Vision-language-action models benefit from the understanding and reasoning capabilities of pretrained vision-language models, but action-only supervision provides limited grounding in world dynamics.

8. InterEvolve: Test-Time Evolution of Reward Programs for Humanoid Loco-Manipulation

We study test-time evolution for humanoid loco-manipulation: solving tasks that a controller was never trained for by repurposing its existing skills, improving from its own attempts, and retaining what it learns, without retraining.

フロンティア基準（キュレーション）

1. Z-1: efficient RL post-training for flow-based VLA models

2. EgoScale: scaling dexterous manipulation with diverse egocentric human data

3. NVIDIA Cosmos 3: physical reasoning, world generation and action generation in one open model

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

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

6. SHIELD: safety on humanoids via control barrier functions in expectation on learned dynamics

軸ごとの分布 (直近 7 日の採択)

軸 #
基盤モデル（VLA / WAM） 11
データエンジン（収集 / 精査 / スケーリング） 3
学習と自己改善（RL / 事後学習） 5
シミュレーションと評価（sim2real / 閉ループ） 12
エッジとリアルタイム（オンデバイス推論 / 制御周期） 9
安全・権限・コンプライアンス 4
システムとオーケストレーション（長期タスク / 複数方策） 4
ハードウェアとサプライチェーン（機体 / コスト） 8

直近の推移

標準・規制ウォッチ

本ページの項目別分析は radar-analyst（claude-opus-5.5）が下書きし、決定的な検査とエージェント査読を経て自動的に取り込まれました。人間の確認は経ていません。タイトルと数値は改変していません。

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。

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