12 citations · 23 across the 9 of their papers we have counts for
9 papers
Rotating without Seeing: Towards In-hand Dexterity through Touch
Zhao-Heng Yin, Binghao Huang, Yuzhe Qin +2
Tactile information plays a critical role in human dexterity. It reveals useful contact information that may not be inferred directly from vision. In fact, humans can even perform…
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation
Yuzhe Qin, Binghao Huang, Zhao-Heng Yin +2
We propose a sim-to-real framework for dexterous manipulation which can generalize to new objects of the same category in the real world. The key of our framework is to train the m…
Planning for Sample Efficient Imitation Learning
Zhao-Heng Yin, Weirui Ye, Qifeng Chen +1
Imitation learning is a class of promising policy learning algorithms that is free from many practical issues with reinforcement learning, such as the reward design issue and the e…
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation
Zhaoyuan Yin, Pichao Wang, Fan Wang +4
Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. Most existing methods are bottom-up ap…
Cross Domain Robot Imitation with Invariant Representation
Zhao-Heng Yin, Lingfeng Sun, Hengbo Ma +2
Animals are able to imitate each others' behavior, despite their difference in biomechanics. In contrast, imitating the other similar robots is a much more challenging task in robo…
Iterative Imitation Policy Improvement for Interactive Autonomous Driving
Zhao-Heng Yin, Chenran Li, Liting Sun +2
We propose an imitation learning system for autonomous driving in urban traffic with interactions. We train a Behavioral Cloning~(BC) policy to imitate driving behavior collected f…
Diverse Critical Interaction Generation for Planning and Planner Evaluation
Zhao-Heng Yin, Lingfeng Sun, Liting Sun +2
Generating diverse and comprehensive interacting agents to evaluate the decision-making modules is essential for the safe and robust planning of autonomous vehicles~(AV). Due to ef…
Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild
Zhaoyuan Yin, Jia Zheng, Weixin Luo +3
This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively. Then, based on the u…
TOMA: Topological Map Abstraction for Reinforcement Learning
Zhao-Heng Yin, Wu-Jun Li
Animals are able to discover the topological map (graph) of surrounding environment, which will be used for navigation. Inspired by this biological phenomenon, researchers have rec…