4 citations · 6 across the 7 of their papers we have counts for
7 papers
Spatio-Temporal Transformer-Based Reinforcement Learning for Robot Crowd Navigation
Haodong He, Hao Fu, Qiang Wang +2
The social robot navigation is an open and challenging problem. In existing work, separate modules are used to capture spatial and temporal features, respectively. However, such me…
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning
Shuhan Qi, Shuhao Zhang, Qiang Wang +3
Value-decomposition methods, which reduce the difficulty of a multi-agent system by decomposing the joint state-action space into local observation-action spaces, have become popul…
Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation Policies
Qiang Wang, Robert McCarthy, David Cordova Bulens +4
This paper presents our solution for the Real Robot Challenge (RRC) III, a competition featured in the NeurIPS 2022 Competition Track, aimed at addressing dexterous robotic manipul…
Improving Behavioural Cloning with Positive Unlabeled Learning
Qiang Wang, Robert McCarthy, David Cordova Bulens +6
Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems. However, available datasets are typically of mixed q…
Hierarchical reinforcement learning for in-hand robotic manipulation using Davenport chained rotations
Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens +3
End-to-end reinforcement learning techniques are among the most successful methods for robotic manipulation tasks. However, the training time required to find a good policy capable…
Imaginary Hindsight Experience Replay: Curious Model-based Learning for Sparse Reward Tasks
Robert McCarthy, Qiang Wang, Stephen J. Redmond
Model-based reinforcement learning is a promising learning strategy for practical robotic applications due to its improved data-efficiency versus model-free counterparts. However,…
Rethinking Performance Estimation in Neural Architecture Search
Xiawu Zheng, Rongrong Ji, Qiang Wang +4
Neural architecture search (NAS) remains a challenging problem, which is attributed to the indispensable and time-consuming component of performance estimation (PE). In this paper,…