6 citations · 11 across the 7 of their papers we have counts for
9 papers
RoboFolDeX: A Physical-World Benchmark for Long-Horizon Robotic Manipulation of Deformable Objects
Chenhuan Liu, Yi Xu, Feng Wu +7
Embodied AI, including vision-language-action and world-action models, must operate reliably in the physical world. Yet methods that perform well in simulation can degrade substant…
OS-Themis: A Scalable Critic Framework for Generalist GUI Rewards
Zehao Li, Zhenyu Wu, Yibo Zhao +11
Reinforcement Learning (RL) has the potential to improve the robustness of GUI agents in stochastic environments, yet training is highly sensitive to the quality of the reward func…
medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functions
Qianyi Xu, Gousia Habib, Feng Wu +5
Reinforcement Learning (RL) offers a powerful framework for optimizing dynamic treatment regimes (DTRs). However, clinical RL is fundamentally bottlenecked by reward engineering: t…
TeleGate: Whole-Body Humanoid Teleoperation via Gated Expert Selection with Motion Prior
Jie Li, Bing Tang, Feng Wu
Real-time whole-body teleoperation is a critical method for humanoid robots to perform complex tasks in unstructured environments. However, developing a unified controller that rob…
Booster Gym: An End-to-End Reinforcement Learning Framework for Humanoid Robot Locomotion
Yushi Wang, Penghui Chen, Xinyu Han +2
Recent advancements in reinforcement learning (RL) have led to significant progress in humanoid robot locomotion, simplifying the design and training of motion policies in simulati…
Effective Multimodal Reinforcement Learning with Modality Alignment and Importance Enhancement
Jinming Ma, Feng Wu, Yingfeng Chen +2
Many real-world applications require an agent to make robust and deliberate decisions with multimodal information (e.g., robots with multi-sensory inputs). However, it is very chal…
Meta Reinforcement Learning with Successor Feature Based Context
Xu Han, Feng Wu
Most reinforcement learning (RL) methods only focus on learning a single task from scratch and are not able to use prior knowledge to learn other tasks more effectively. Context-ba…
Automatic Reward Design via Learning Motivation-Consistent Intrinsic Rewards
Yixiang Wang, Yujing Hu, Feng Wu +1
Reward design is a critical part of the application of reinforcement learning, the performance of which strongly depends on how well the reward signal frames the goal of the design…
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control
Jinming Ma, Feng Wu
Multi-agent reinforcement learning (MARL) has been applied and shown great potential in multi-intersections traffic signal control, where multiple agents, one for each intersection…