7 papers
GPUSimBench: Towards Scalable and Reliable GPU-Accelerated Simulators in Embodied AI
Huzhenyu Zhang, Shenghai Yuan, Wenrui Yan +4
Data-driven embodied AI is rapidly transitioning into a paradigm that scales training through massively parallel simulation, where GPU-accelerated simulators serve as the foundatio…
VLGOR: Visual-Language Knowledge Guided Offline Reinforcement Learning for Generalizable Agents
Pengsen Liu, Maosen Zeng, Nan Tang +4
Combining Large Language Models (LLMs) with Reinforcement Learning (RL) enables agents to interpret language instructions more effectively for task execution. However, LLMs typical…
ReLAM: Learning Anticipation Model for Rewarding Visual Robotic Manipulation
Nan Tang, Jing-Cheng Pang, Guanlin Li +2
Reward design remains a critical bottleneck in visual reinforcement learning (RL) for robotic manipulation. In simulated environments, rewards are conventionally designed based on…
ImagineBench: Evaluating Reinforcement Learning with Large Language Model Rollouts
Jing-Cheng Pang, Kaiyuan Li, Yidi Wang +3
A central challenge in reinforcement learning (RL) is its dependence on extensive real-world interaction data to learn task-specific policies. While recent work demonstrates that l…
WHALE: Towards Generalizable and Scalable World Models for Embodied Decision-making
Zhilong Zhang, Ruifeng Chen, Junyin Ye +8
World models play a crucial role in decision-making within embodied environments, enabling cost-free explorations that would otherwise be expensive in the real world. To facilitate…
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization
Jing-Cheng Pang, Tian Xu, Shengyi Jiang +2
Reinforcement learning (RL) has demonstrated impressive performance in decision-making tasks like embodied control, autonomous driving and financial trading. In many decision-makin…