5 papers
SLoSa: Small, Low-Power, and Safe Foundation Models for Robotic Task Planning
Weizhe Xu, Jialiang Fan, Essien Taylor +3
Safe robotic task planning requires planners that can reliably achieve task goals while satisfying safety constraints. Classical planners search over explicit symbolic task represe…
OntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition
Xiangqing Lu, Tao Zhu, Liang Yu +6
LLMs increasingly support sensor-based human activity recognition (HAR) by linking motion signals with activity semantics and physical knowledge. Retrieval-augmented generation (RA…
RedFlow: Redirect Failure into Action-level Corrections for Flow-matching VLA Policy
Zhengyang Yan, Junhao Li, Fangqi Zhu +6
Reinforcement learning (RL) can improve Vision-Language-Action (VLA) policies from deployment experience, but reward- and preference-based RL primarily identifies desirable behavio…
PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration
Han Wang, Zijun Wang, Shuoshuo Xue +5
Action-conditioned world models are a key component of embodied AI, serving as scalable policy evaluators that reduce reliance on expensive real-world rollouts. To accurately captu…
PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models
Bin Hu, Yanwen Ma, Jiehui Huang +14
Recent game world models can synthesize visually plausible, action-conditioned rollouts. However, their interaction behaviors often remain limited to exploratory or wandering traje…