1 citations · 3 across the 21 of their papers we have counts for
19 papers · 1 filter
GenHOI: Contact-Aware Humanoid-Object Interaction by Imitating Generated Videos without Task-Specific Training
Zhihai Bi, Qiang Zhang, Guoyang Zhao +8
Humanoid-Object Interaction (HOI) is a fundamental capability for humanoid robots, yet it remains challenging due to the tight coupling between dynamic balance and stable interacti…
Safe Navigation in Unknown and Cluttered Environments via Direction-Aware Convex Free-Region Generation
Zhicheng Song, Yongjian Li, Kai Chen +3
Convex free regions provide a structured and optimization-friendly representation of collision-free space for robot navigation in unknown and cluttered environments. However, exist…
ALORE: Autonomous Large-Object Rearrangement with a Legged Manipulator
Zhihai Bi, Yushan Zhang, Kai Chen +3
Endowing robots with the ability to rearrange various large and heavy objects, such as furniture, can substantially alleviate human workload. However, this task is extremely challe…
Fast and Safe Trajectory Optimization for Mobile Manipulators With Neural Configuration Space Distance Field
Yulin Li, Zhiyuan Song, Yiming Li +8
Mobile manipulators promise agile, long-horizon behavior by coordinating base and arm motion, yet whole-body trajectory optimization in cluttered, confined spaces remains difficult…
Online Trajectory Optimization for Arbitrary-Shaped Mobile Robots via Polynomial Separating Hypersurfaces
Shuoye Li, Zhiyuan Song, Yulin Li +2
An emerging class of trajectory optimization methods enforces collision avoidance by jointly optimizing the robot's configuration and a separating hyperplane. However, as linear se…
Embracing Bulky Objects with Humanoid Robots: Whole-Body Manipulation with Reinforcement Learning
Chunxin Zheng, Kai Chen, Zhihai Bi +5
Whole-body manipulation (WBM) for humanoid robots presents a promising approach for executing embracing tasks involving bulky objects, where traditional grasping relying on end-eff…
SAMP: Spatial Anchor-based Motion Policy for Collision-Aware Robotic Manipulators
Kai Chen, Zhihai Bi, Guoyang Zhao +4
Neural-based motion planning methods have achieved remarkable progress for robotic manipulators, yet a fundamental challenge lies in simultaneously accounting for both the robot's…
A Reconfigured Wheel-Legged Robot for Enhanced Steering and Adaptability
Zhicheng Song, Jinglan Xu, Chunxin Zheng +3
Wheel-legged robots integrate leg agility on rough terrain with wheel efficiency on flat ground. However, most existing designs do not fully capitalize on the benefits of both legg…
Interactive Navigation for Legged Manipulators with Learned Arm-Pushing Controller
Zhihai Bi, Kai Chen, Chunxin Zheng +3
Interactive navigation is crucial in scenarios where proactively interacting with objects can yield shorter paths, thus significantly improving traversal efficiency. Existing metho…
Local Reactive Control for Mobile Manipulators with Whole-Body Safety in Complex Environments
Chunxin Zheng, Yulin Li, Zhiyuan Song +4
Mobile manipulators typically encounter significant challenges in navigating narrow, cluttered environments due to their high-dimensional state spaces and complex kinematics. While…
UDMC: Unified Decision-Making and Control Framework for Urban Autonomous Driving with Motion Prediction of Traffic Participants
Haichao Liu, Kai Chen, Yulin Li +3
Current autonomous driving systems often struggle to balance decision-making and motion control while ensuring safety and traffic rule compliance, especially in complex urban envir…
FRTree Planner: Robot Navigation in Cluttered and Unknown Environments with Tree of Free Regions
Yulin Li, Zhicheng Song, Chunxin Zheng +4
In this work, we present FRTree planner, a novel robot navigation framework that leverages a tree structure of free regions, specifically designed for navigation in cluttered and u…
Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models
Jiahang Cao, Qiang Zhang, Jingkai Sun +11
Diffusion models have been widely employed in the field of 3D manipulation due to their efficient capability to learn distributions, allowing for precise prediction of action traje…
Collision-Free Trajectory Optimization in Cluttered Environments Using Sums-of-Squares Programming
Yulin Li, Chunxin Zheng, Kai Chen +4
In this work, we propose a trajectory optimization approach for robot navigation in cluttered 3D environments. We represent the robot's geometry as a semialgebraic set defined by p…
Robot Navigation in Unknown and Cluttered Workspace with Dynamical System Modulation in Starshaped Roadmap
Kai Chen, Haichao Liu, Yulin Li +3
Compared to conventional decomposition methods that use ellipses or polygons to represent free space, starshaped representation can better capture the natural distribution of senso…
Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles with Limited Communication
Haichao Liu, Zhenmin Huang, Zicheng Zhu +3
This paper investigates a cooperative motion planning problem for large-scale connected autonomous vehicles (CAVs) under limited communications, which addresses the challenges of h…
Geometry-Aware Safety-Critical Local Reactive Controller for Robot Navigation in Unknown and Cluttered Environments
Yulin Li, Xindong Tang, Kai Chen +3
This work proposes a safety-critical local reactive controller that enables the robot to navigate in unknown and cluttered environments. In particular, the trajectory tracking task…
Integrated Behavior Planning and Motion Control for Autonomous Vehicles with Traffic Rules Compliance
Haichao Liu, Kai Chen, Yulin Li +3
In this article, we propose an optimization-based integrated behavior planning and motion control scheme, which is an interpretable and adaptable urban autonomous driving solution…
Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning
Yubin Wang, Yulin Li, Zengqi Peng +2
Lane change in dense traffic typically requires the recognition of an appropriate opportunity for maneuvers, which remains a challenging problem in self-driving. In this work, we p…