5 papers · 1 filter
Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning
Shilin Shan, Chuhao Zhou, Ruize Wang +30
Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world. This capability is particularly critical…
ADAPT: Analytical Disturbance-Aware Policy Training for Humanoid Locomotion
Bofan Lyu, Jindou Jia, Kuangji Zuo +7
Humanoids deployed in human-centered environments must handle force-interactive tasks, where external contacts introduce unexpected disturbances that disrupt locomotion accuracy an…
MARS Policy: Multimodality Only When It Matters
Jindou Jia, Tuo An, Yuxuan Hu +7
Imitation learning has become a cornerstone for solving complex robotic manipulation tasks. In particular, multimodality, which enables robots to capture diverse yet valid behavior…
Feedback World Model Enables Precise Guidance of Diffusion Policy
Tuo An, Jindou Jia, Gen Li +8
World models aim to improve robotic decision making by predicting the consequences of actions. However, in practice, their predictions often become unreliable once the robot encoun…
Action-to-Action Flow Matching
Jindou Jia, Gen Li, Xiangyu Chen +5
Diffusion-based policies have recently achieved remarkable success in robotics by formulating action prediction as a conditional denoising process. However, the standard practice o…