43 citations · 70 across the 15 of their papers we have counts for
10 papers · 1 filter
Embodied-Navigator: Point, Think, Memorize, and Align for Efficient Navigation
Hongyan Feng, Sunlai Chen, Xuanyu Liu +9
Although Large Vision-Language Models (VLMs) have significantly advanced embodied navigation, their direct deployment remains challenging, as existing methods often force VLMs into…
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…
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…
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…
Learning adaptive manipulation of objects with revolute joint: A case study on varied cabinet doors opening
Hongxiang Yu, Dashun Guo, Zhongxiang Zhou +2
This paper introduces a learning-based framework for robot adaptive manipulating the object with a revolute joint in unstructured environments. We concentrate our discussion on var…
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…
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 to Fill the Seam by Vision: Sub-millimeter Peg-in-hole on Unseen Shapes in Real World
Liang Xie, Hongxiang Yu, Yinghao Zhao +5
In the peg insertion task, human pays attention to the seam between the peg and the hole and tries to fill it continuously with visual feedback. By imitating the human behavior, we…