103 citations · 184 across the 24 of their papers we have counts for
24 papers
Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
Xinghao Zhu, Zixi Liu, Shalin Jain +18
Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging. We present Contact…
Robust Model-Based In-Hand Manipulation with Integrated Real-Time Motion-Contact Planning and Tracking
Yongpeng Jiang, Mingrui Yu, Xinghao Zhu +2
Robotic dexterous in-hand manipulation, where multiple fingers dynamically make and break contact, represents a step toward human-like dexterity in real-world robotic applications.…
Residual Policy Gradient: A Reward View of KL-regularized Objective
Pengcheng Wang, Xinghao Zhu, Yuxin Chen +3
Reinforcement Learning and Imitation Learning have achieved widespread success in many domains but remain constrained during real-world deployment. One of the main issues is the ad…
DexH2R: Task-oriented Dexterous Manipulation from Human to Robots
Shuqi Zhao, Xinghao Zhu, Yuxin Chen +5
Dexterous manipulation is a critical aspect of human capability, enabling interaction with a wide variety of objects. Recent advancements in learning from human demonstrations and…
Adaptive Energy Regularization for Autonomous Gait Transition and Energy-Efficient Quadruped Locomotion
Boyuan Liang, Lingfeng Sun, Xinghao Zhu +6
In reinforcement learning for legged robot locomotion, crafting effective reward strategies is crucial. Pre-defined gait patterns and complex reward systems are widely used to stab…
In-Hand Following of Deformable Linear Objects Using Dexterous Fingers with Tactile Sensing
Mingrui Yu, Boyuan Liang, Xiang Zhang +6
Most research on deformable linear object (DLO) manipulation assumes rigid grasping. However, beyond rigid grasping and re-grasping, in-hand following is also an essential skill th…
PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models
Dingkun Guo, Yuqi Xiang, Shuqi Zhao +4
Robotic grasping is a fundamental aspect of robot functionality, defining how robots interact with objects. Despite substantial progress, its generalizability to counter-intuitive…
Contact-Implicit Model Predictive Control for Dexterous In-hand Manipulation: A Long-Horizon and Robust Approach
Yongpeng Jiang, Mingrui Yu, Xinghao Zhu +2
Dexterous in-hand manipulation is an essential skill of production and life. However, the highly stiff and mutable nature of contacts limits real-time contact detection and inferen…
Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection
Xinghao Zhu, Devesh K. Jha, Diego Romeres +3
Automating the assembly of objects from their parts is a complex problem with innumerable applications in manufacturing, maintenance, and recycling. Unlike existing research, which…
Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks
Lingfeng Sun, Devesh K. Jha, Chiori Hori +5
Designing robotic agents to perform open vocabulary tasks has been the long-standing goal in robotics and AI. Recently, Large Language Models (LLMs) have achieved impressive result…
Control of Soft Pneumatic Actuators with Approximated Dynamical Modeling
Wu-Te Yang, Burak Kurkcu, Motohiro Hirao +5
This paper introduces a full system modeling strategy for a syringe pump and soft pneumatic actuators(SPAs). The soft actuator is conceptualized as a beam structure, utilizing a se…
Efficient Sim-to-real Transfer of Contact-Rich Manipulation Skills with Online Admittance Residual Learning
Xiang Zhang, Changhao Wang, Lingfeng Sun +3
Learning contact-rich manipulation skills is essential. Such skills require the robots to interact with the environment with feasible manipulation trajectories and suitable complia…
Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation
Yuqi Xiang, Feitong Chen, Qinsi Wang +5
The capability to transfer mastered skills to accomplish a range of similar yet novel tasks is crucial for intelligent robots. In this work, we introduce ,…
Human-oriented Representation Learning for Robotic Manipulation
Mingxiao Huo, Mingyu Ding, Chenfeng Xu +6
Humans inherently possess generalizable visual representations that empower them to efficiently explore and interact with the environments in manipulation tasks. We advocate that s…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
A Simple Approach for General Task-Oriented Picking using Placing constraints
Jen-Wei Wang, Lingfeng Sun, Xinghao Zhu +2
Pick-and-place is an important manipulation task in domestic or manufacturing applications. There exist many works focusing on grasp detection with high picking success rate but la…
Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces
Xinghao Zhu, Wenzhao Lian, Bodi Yuan +2
In recent years, impressive results have been achieved in robotic manipulation. While many efforts focus on generating collision-free reference signals, few allow safe contact betw…
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints
Xinghao Zhu, Siddarth Jain, Masayoshi Tomizuka +1
Vision-based tactile sensors typically utilize a deformable elastomer and a camera mounted above to provide high-resolution image observations of contacts. Obtaining accurate volum…
Offline-Online Learning of Deformation Model for Cable Manipulation with Graph Neural Networks
Changhao Wang, Yuyou Zhang, Xiang Zhang +5
Manipulating deformable linear objects by robots has a wide range of applications, e.g., manufacturing and medical surgery. To complete such tasks, an accurate dynamics model for p…
Learning Insertion Primitives with Discrete-Continuous Hybrid Action Space for Robotic Assembly Tasks
Xiang Zhang, Shiyu Jin, Changhao Wang +2
This paper introduces a discrete-continuous action space to learn insertion primitives for robotic assembly tasks. Primitive is a sequence of elementary actions with certain exit c…
Learn to Grasp with Less Supervision: A Data-Efficient Maximum Likelihood Grasp Sampling Loss
Xinghao Zhu, Yefan Zhou, Yongxiang Fan +3
Robotic grasping for a diverse set of objects is essential in many robot manipulation tasks. One promising approach is to learn deep grasping models from large training datasets of…
6-DoF Contrastive Grasp Proposal Network
Xinghao Zhu, Lingfeng Sun, Yongxiang Fan +1
Proposing grasp poses for novel objects is an essential component for any robot manipulation task. Planning six degrees of freedom (DoF) grasps with a single camera, however, is ch…
Contact Pose Identification for Peg-in-Hole Assembly under Uncertainties
Shiyu Jin, Xinghao Zhu, Changhao Wang +1
Peg-in-hole assembly is a challenging contact-rich manipulation task. There is no general solution to identify the relative position and orientation between the peg and the hole. I…
Optimization Model for Planning Precision Grasps with Multi-Fingered Hands
Yongxiang Fan, Xinghao Zhu, Masayoshi Tomizuka
Precision grasps with multi-fingered hands are important for precise placement and in-hand manipulation tasks. Searching precision grasps on the object represented by point cloud,…