Physical AI 前沿雷达 · 2026-10-12

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. LiteNWM: Efficient Latent World Models for Onboard Visual Navigation in the Wild

Direct visual navigation policies generate trajectories efficiently but do not explicitly evaluate their future consequences.

2. RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes

Embodied coding agents can combine modular robot skills with frozen end-to-end policies, yet effective composition requires anticipating which policy family will succeed in the current physical state.

3. Generative Neural Retargeting for Human-to-Robot Dexterous Manipulation

Human demonstrations are a scalable data source for learning dexterous manipulation, but the embodiment gap prevents human motion from being executed directly on robots.

4. LeWAM: A JEPA World Action Model with Diffusion-Steering-Based MPC

World action models (WAMs) predict actions and future observations, typically from a reconstruction-based representation that carries noisy, redundant information which can complicate downstream predictions.

5. SpatialHarness: Test-Time Spatial Scaffolding for Fine Robotic Manipulation

Frontier multimodal foundation models (e.g., GPT-6 Astra) have recently shown strong potential for direct robotic control, yet their performance on fine manipulation remains limited.

6. Real-Time Motion Planning with Dynamic Hazards: Classical vs. Learning-Based Methods

We study real-time motion planning in dynamic hazard fields through a controlled comparison between classical planning and learning-based methods.

7. WAND: Learning Robust Navigation under Complex Wind Disturbances and Dense Obstacles for Quadrotors

Robust navigation in cluttered environments remains a fundamental challenge for quadrotors, particularly when strong wind disturbances arise, which perturb vehicle dynamics, limit control authority, and substantially increase collision…

8. CSF: Contextual Safety Filtering for Motion Generators

Text-conditioned motion generators produce trackable whole-body motion, but they have no notion of scene-dependent safety: the same action may target an object or a person.

前沿基线（人工策展）

1. RD-VLA: implicit test-time compute scaling via latent iterative reasoning

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

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

4. Isaac Lab-Arena: reusable APIs to author, scale and analyse robot policy evaluations

5. Egocentric human video can outperform real-robot data for embodied pretraining

6. HumanEgo: zero-shot robot learning from minutes of human egocentric video

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） 5
训练与自我改进（RL / 后训练） 4
仿真与评测（sim2real / 闭环） 14
边缘与实时性（端侧推理 / 控制频率） 7
安全、权限与合规 3
系统与编排（长程 / 多策略） 2
本体与供应链（硬件 / 成本） 10

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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