4 citations · 11 across the 18 of their papers we have counts for
15 papers · 1 filter
APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
Kechun Xu, Zhenjie Zhu, Anzhe Chen +2
Vision-Language-Action (VLA) models that couple pretrained Vision-Language Models (VLMs) with continuous action experts have achieved strong manipulation performance, yet generaliz…
TDC: Sim-to-Real Transferable Directional Compliance for Contact-Rich Manipulation
Yifei Yang, Anzhe Chen, Zhenjie Zhu +6
Contact-rich manipulation requires robots to regulate both motion and interaction forces, yet achieving adaptive compliance remains a fundamental challenge. Learning from real-worl…
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…
ExploreVLM: Closed-Loop Robot Exploration Task Planning with Vision-Language Models
Zhichen Lou, Kechun Xu, Zhongxiang Zhou +1
The advancement of embodied intelligence is accelerating the integration of robots into daily life as human assistants. This evolution requires robots to not only interpret high-le…
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…
Grasp, See, and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior
Kechun Xu, Zhongxiang Zhou, Jun Wu +3
We focus on the task of unknown object rearrangement, where a robot is supposed to re-configure the objects into a desired goal configuration specified by an RGB-D image. Recent wo…
A Hyper-network Based End-to-end Visual Servoing with Arbitrary Desired Poses
Hongxiang Yu, Anzhe Chen, Kechun Xu +4
Recently, several works achieve end-to-end visual servoing (VS) for robotic manipulation by replacing traditional controller with differentiable neural networks, but lose the abili…
Object-centric Inference for Language Conditioned Placement: A Foundation Model based Approach
Zhixuan Xu, Kechun Xu, Yue Wang +1
We focus on the task of language-conditioned object placement, in which a robot should generate placements that satisfy all the spatial relational constraints in language instructi…
Failure-aware Policy Learning for Self-assessable Robotics Tasks
Kechun Xu, Runjian Chen, Shuqi Zhao +5
Self-assessment rules play an essential role in safe and effective real-world robotic applications, which verify the feasibility of the selected action before actual execution. But…
A Joint Modeling of Vision-Language-Action for Target-oriented Grasping in Clutter
Kechun Xu, Shuqi Zhao, Zhongxiang Zhou +4
We focus on the task of language-conditioned grasping in clutter, in which a robot is supposed to grasp the target object based on a language instruction. Previous works separately…
Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes
Liang Xie, Hongxiang Yu, Kechun Xu +5
This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation, and adapting to arbitrary unseen shapes in real world with minimal…
Efficient Object Manipulation to an Arbitrary Goal Pose: Learning-based Anytime Prioritized Planning
Kechun Xu, Hongxiang Yu, Renlang Huang +3
We focus on the task of object manipulation to an arbitrary goal pose, in which a robot is supposed to pick an assigned object to place at the goal position with a specific orienta…
Neural Motion Prediction for In-flight Uneven Object Catching
Hongxiang Yu, Dashun Guo, Huan Yin +4
In-flight objects capture is extremely challenging. The robot is required to complete trajectory prediction, interception position calculation and motion planning in sequence withi…
Efficient learning of goal-oriented push-grasping synergy in clutter
Kechun Xu, Hongxiang Yu, Qianen Lai +2
We focus on the task of goal-oriented grasping, in which a robot is supposed to grasp a pre-assigned goal object in clutter and needs some pre-grasp actions such as pushes to enabl…