4 citations · 11 across the 18 of their papers we have counts for
18 papers
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…
Grounding 3D Object Affordance with Language Instructions, Visual Observations and Interactions
He Zhu, Quyu Kong, Kechun Xu +5
Grounding 3D object affordance is a task that locates objects in 3D space where they can be manipulated, which links perception and action for embodied intelligence. For example, f…
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…
UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation
Longzhong Lin, Xuewu Lin, Kechun Xu +4
Simulation plays a crucial role in assessing autonomous driving systems, where the generation of realistic multi-agent behaviors is a key aspect. In multi-agent simulation, the pri…
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…
E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context
Zizhang Li, Mengmeng Wang, Huaijin Pi +3
Recently, the image-wise implicit neural representation of videos, NeRV, has gained popularity for its promising results and swift speed compared to regular pixel-wise implicit rep…
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…