activity
20182022
most citedImitation Learning from Observations by Minimizing Inverse Dynamics Disagreement

27 citations · 59 across the 6 of their papers we have counts for

collaborators

10 papers

cs.CV20222 cited

Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation

Jiangyong Huang, William Yicheng Zhu, Baoxiong Jia +4

Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding. Existing efforts to create a general vision model are limit…

cs.LG20219 cited

Adversarial Option-Aware Hierarchical Imitation Learning

Mingxuan Jing, Wenbing Huang, Fuchun Sun +4

It has been a challenge to learning skills for an agent from long-horizon unannotated demonstrations. Existing approaches like Hierarchical Imitation Learning(HIL) are prone to com…

cs.LG20218 cited

HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving

Sirui Xie, Xiaojian Ma, Peiyu Yu +3

Humans learn compositional and causal abstraction, \ie, knowledge, in response to the structure of naturalistic tasks. When presented with a problem-solving task involving some obj…

cs.RO2020

A Mobile Robot Hand-Arm Teleoperation System by Vision and IMU

Shuang Li, Jiaxi Jiang, Philipp Ruppel +5

In this paper, we present a multimodal mobile teleoperation system that consists of a novel vision-based hand pose regression network (Transteleop) and an IMU-based arm tracking me…

cs.LG20194 cited

Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning

Mark Edmonds, Xiaojian Ma, Siyuan Qi +3

Learning transferable knowledge across similar but different settings is a fundamental component of generalized intelligence. In this paper, we approach the transfer learning chall…

cs.LG20199 cited

Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance

Mingxuan Jing, Xiaojian Ma, Wenbing Huang +4

In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations…