7 papers
PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation
Lingxuan Wu, Zijian Zhu, Lizhong Wang +5
Diffusion policies have achieved remarkable success in robotic manipulation, yet they often fail to satisfy strict physical constraints required for safe deployment. Existing appro…
Task Aware Dreamer for Task Generalization in Reinforcement Learning
Chengyang Ying, Xinning Zhou, Zhongkai Hao +4
A long-standing goal of reinforcement learning is to acquire agents that can learn on training tasks and generalize well on unseen tasks that may share a similar dynamic but with d…
ManiBox: Enhancing Embodied Spatial Generalization via Scalable Simulation Data Generations
Hengkai Tan, Xuezhou Xu, Chengyang Ying +7
Embodied agents require robust spatial intelligence to execute precise real-world manipulations. However, this remains a significant challenge, as current methods often struggle to…
ASearch: Ambiguity-Aware Question Answering with Reinforcement Learning
Fengji Zhang, Xinyao Niu, Chengyang Ying +7
Recent advances in Large Language Models (LLMs) and Reinforcement Learning (RL) have led to strong performance in open-domain question answering (QA). However, existing models stil…
Reinforced Embodied Active Defense: Exploiting Adaptive Interaction for Robust Visual Perception in Adversarial 3D Environments
Xiao Yang, Lingxuan Wu, Lizhong Wang +3
Adversarial attacks in 3D environments have emerged as a critical threat to the reliability of visual perception systems, particularly in safety-sensitive applications such as iden…
Exploratory Diffusion Model for Unsupervised Reinforcement Learning
Chengyang Ying, Huayu Chen, Xinning Zhou +3
Unsupervised reinforcement learning (URL) aims to pre-train agents by exploring diverse states or skills in reward-free environments, facilitating efficient adaptation to downstrea…