64 citations · 96 across the 18 of their papers we have counts for
18 papers
DexCtrl: Towards Sim-to-Real Dexterity with Adaptive Controller Learning
Shuqi Zhao, Ke Yang, Yuxin Chen +5
Dexterous manipulation has seen remarkable progress in recent years, with policies capable of executing many complex and contact-rich tasks in simulation. However, transferring the…
Imagined Potential Games: A Framework for Simulating, Learning and Evaluating Interactive Behaviors
Lingfeng Sun, Yixiao Wang, Pin-Yun Hung +4
Interacting with human agents in complex scenarios presents a significant challenge for robotic navigation, particularly in environments that necessitate both collision avoidance a…
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…
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…
Distributed Multi-agent Interaction Generation with Imagined Potential Games
Lingfeng Sun, Pin-Yun Hung, Changhao Wang +2
Interactive behavior modeling of multiple agents is an essential challenge in simulation, especially in scenarios when agents need to avoid collisions and cooperate at the same tim…
Generalizable whole-body global manipulation of deformable linear objects by dual-arm robot in 3-D constrained environments
Mingrui Yu, Kangchen Lv, Changhao Wang +3
Constrained environments are common in practical applications of manipulating deformable linear objects (DLOs), where movements of both DLOs and robots should be constrained. This…
Contact-rich SE(3)-Equivariant Robot Manipulation Task Learning via Geometric Impedance Control
Joohwan Seo, Nikhil Potu Surya Prakash, Xiang Zhang +4
This paper presents a differential geometric control approach that leverages SE(3) group invariance and equivariance to increase transferability in learning robot manipulation task…
Learning from Local Experience: Informed Sampling Distributions for High Dimensional Motion Planning
Keita Kobashi, Changhao Wang, Yu Zhao +2
This paper presents a sampling-based motion planning framework that leverages the geometry of obstacles in a workspace as well as prior experiences from motion planning problems. P…
Continuous Trajectory Optimization via B-splines for Multi-jointed Robotic Systems
Changhao Wang, Ting Xu, Masayoshi Tomizuka
Continuous formulations of trajectory planning problems have two main benefits. First, constraints are guaranteed to be satisfied at all times. Secondly, dynamic obstacles can be n…
Prim-LAfD: A Framework to Learn and Adapt Primitive-Based Skills from Demonstrations for Insertion Tasks
Zheng Wu, Wenzhao Lian, Changhao Wang +3
Learning generalizable insertion skills in a data-efficient manner has long been a challenge in the robot learning community. While the current state-of-the-art methods with reinfo…
Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning
Zheng Wu, Yichen Xie, Wenzhao Lian +5
Humans are capable of abstracting various tasks as different combinations of multiple attributes. This perspective of compositionality is vital for human rapid learning and adaptio…
A Coarse-to-Fine Framework for Dual-Arm Manipulation of Deformable Linear Objects with Whole-Body Obstacle Avoidance
Mingrui Yu, Kangchen Lv, Changhao Wang +2
Manipulating deformable linear objects (DLOs) to achieve desired shapes in constrained environments with obstacles is a meaningful but challenging task. Global planning is necessar…
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…
Trajectory Splitting: A Distributed Formulation for Collision Avoiding Trajectory Optimization
Changhao Wang, Jeffrey Bingham, Masayoshi Tomizuka
Efficient trajectory optimization is essential for avoiding collisions in unstructured environments, but it remains challenging to have both speed and quality in the solutions. One…
Safe Online Gain Optimization for Variable Impedance Control
Changhao Wang, Zhian Kuang, Xiang Zhang +1
Smooth behaviors are preferable for many contact-rich manipulation tasks. Impedance control arises as an effective way to regulate robot movements by mimicking a mass-spring-dampin…
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…
Development of Soft Tactile Sensor for Force Measurement and Position Detection
Wu-Te Yang, Zhian Kuang, Changhao Wang +1
As more robots are implemented for contact-rich tasks, tactile sensors are in increasing demand. For many circumstances, the contact is required to be compliant, and soft sensors a…
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…