5 papers
KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policies
Yihan Zeng, Minghao Ye, Yiyuan Chen +4
Long-horizon robot manipulation remains challenging because similar observations may occur at different execution stages, while the appropriate action depends on previously complet…
SARM2: Multi-Task Stage Aware Reward Modeling for Self Improving Robotic Manipulation
Qianzhong Chen, Hau Zheng, Justin Yu +8
Fine-tuning vision-language-action (VLA) policies for long-horizon manipulation still relies heavily on behavior cloning, which requires costly high-quality demonstrations and keep…
EgoMI: Learning Active Vision and Whole-Body Manipulation from Egocentric Human Demonstrations
Justin Yu, Yide Shentu, Di Wu +3
Imitation learning from human demonstrations offers a promising approach for robot skill acquisition, but egocentric human data introduces fundamental challenges due to the embodim…
From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control
Yide Shentu, Philipp Wu, Aravind Rajeswaran +1
Hierarchical control for robotics has long been plagued by the need to have a well defined interface layer to communicate between high-level task planners and low-level policies. W…
RoboCopilot: Human-in-the-loop Interactive Imitation Learning for Robot Manipulation
Philipp Wu, Yide Shentu, Qiayuan Liao +5
Learning from human demonstration is an effective approach for learning complex manipulation skills. However, existing approaches heavily focus on learning from passive human demon…