5 papers
Seeing to Act, Prompting to Specify: A Bayesian Factorization of Vision Language Action Policy
Kechun Xu, Zhenjie Zhu, Anzhe Chen +7
The pursuit of out-of-distribution generalization in Vision-Language-Action (VLA) models is often hindered by catastrophic forgetting of the Vision-Language Model (VLM) backbone du…
Toward Embodiment Equivariant Vision-Language-Action Policy
Anzhe Chen, Yifei Yang, Zhenjie Zhu +4
Vision-language-action policies learn manipulation skills across tasks, environments and embodiments through large-scale pre-training. However, their ability to generalize to novel…
TOP: Time Optimization Policy for Stable and Accurate Standing Manipulation with Humanoid Robots
Zhenghan Chen, Haocheng Xu, Haodong Zhang +7
Humanoid robots have the potential capability to perform a diverse range of manipulation tasks, but this is based on a robust and precise standing controller. Existing methods are…
Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning
Yifei Yang, Lu Chen, Zherui Song +5
Grasp-based manipulation tasks are fundamental to robots interacting with their environments, yet gripper state ambiguity significantly reduces the robustness of imitation learning…
Efficient Alignment of Unconditioned Action Prior for Language-conditioned Pick and Place in Clutter
Kechun Xu, Xunlong Xia, Kaixuan Wang +6
We study the task of language-conditioned pick and place in clutter, where a robot should grasp a target object in open clutter and move it to a specified place. Some approaches le…