most citedDistributed Multi-agent Interaction Generation with Imagined Potential Games

3 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO20232 cited

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…

cs.RO20231 cited

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…

cs.RO20233 cited

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…

cs.RO2023

Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework

Lingfeng Sun, Haichao Zhang, Wei Xu +1

In this work, we investigate the potential of improving multi-task training and also leveraging it for transferring in the reinforcement learning setting. We identify several chall…

cs.RO20232 cited

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…