Physical AI 前沿雷达 · 2026-09-19

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。

lane distribution

今日重点

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…

前沿基线（人工策展）

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

主线分布

主线 #
基座模型（VLA / WAM） 8
数据引擎（采集 / 清洗 / scaling） 6
训练与自我改进（RL / 后训练） 3
仿真与评测（sim2real / 闭环） 5
边缘与实时性（端侧推理 / 控制频率） 7
安全、权限与合规 5
系统与编排（长程 / 多策略） 2
本体与供应链（硬件 / 成本） 1

近期节奏

cadence

evidence mix

合规与标准观察

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。

方法论 · 历史归档