9 papers
P-BFM: Perceptive Behavioral Foundation Models for Humanoid Control via Unsupervised Reinforcement Learning
Xue Wang, Yitang Li, Yiming Zhang +7
Behavioral foundation models (BFMs) provide humanoids with reusable repertoires of whole-body behaviors; in real-world humanoid control, the same behavioral intent may require fund…
From Hitter to Player: Separating Motor Execution and Strategy in Competitive Humanoid Table Tennis
Fanrong Dong, Ziyi Sun, Ziqi Han +6
Competitive humanoid table tennis requires two distinct abilities: executing a desired return and deciding where to return the ball. We separate these two problems, with the ball l…
I-BFM: Reward-Conditioned Robust Humanoid Interaction via Unsupervised Reinforcement Learning
Ziqi Han, Yitang Li, Junhan Sun +8
Behavioral foundation models (BFMs) have recently shown that a single humanoid policy can support diverse whole-body control, but extending such generality to physical interaction…
FlexiWorld: Learning and Planning via Flexible Action Chunks Across Multiple Time Scales
Shidu Ren, Qilin Gu, Zhenghao Ni +4
Latent world models predict future states for goal-directed planning using action chunks spanning multiple primitive steps. Existing methods typically use fixed-length chunks and e…
PredActor: Predictive Action Diffusion for Steerable Onboard Humanoid Control
Lei Ye, Haibo Gao, Yitang Li +10
Diffusion models provide a flexible framework for motion generation, but turning this flexibility into closed-loop humanoid control remains challenging. Hierarchical generator-trac…
INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models
Junhan Sun, Hao Zhao, Guofeng Zhang
Forward latent world models predict how actions change a scene, but recover actions for a desired change only through expensive test-time search. We introduce INTACT (INtent-To-ACT…
LAMP: Lift Image-Editing as General 3D Priors for Open-world Manipulation
Jingjing Wang, Zhengdong Hong, Chong Bao +3
Human-like generalization in open-world remains a fundamental challenge for robotic manipulation. Existing learning-based methods, including reinforcement learning, imitation learn…
Force-Aware Residual DAgger via Trajectory Editing for Precision Insertion with Impedance Control
Yiou Huang, Ning Ma, Weichu Zhao +4
Imitation learning (IL) has shown strong potential for contact-rich precision insertion tasks. However, its practical deployment is often hindered by covariate shift and the need f…
PALo: Learning Posture-Aware Locomotion for Quadruped Robots
Xiangyu Miao, Jun Sun, Hang Lai +4
With the rapid development of embodied intelligence, locomotion control of quadruped robots on complex terrains has become a research hotspot. Unlike traditional locomotion control…