Physical AI 前沿雷达 · 2026-10-05

语言：中文 · EN · JA

每日自动更新的具身智能前沿雷达：八条主线、三语精炼、证据可查。

今日重点

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.

前沿基线（人工策展）

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

主线分布 (近 7 天入选)

主线 #
基座模型（VLA / WAM） 11
数据引擎（采集 / 清洗 / scaling） 3
训练与自我改进（RL / 后训练） 5
仿真与评测（sim2real / 闭环） 12
边缘与实时性（端侧推理 / 控制频率） 9
安全、权限与合规 4
系统与编排（长程 / 多策略） 4
本体与供应链（硬件 / 成本） 8

近期节奏

合规与标准观察

本页逐条分析由 radar-analyst 起草（claude-opus-5.5），经确定性检查与 agent 复核后自动合并，未经人工复核；标题与数字未经改写。

本仓库为技术观察，不构成投资建议、产品承诺或安全认证结论。论文结论以作者自报为准。

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