4 papers
PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty
Wenjing Tang, Xuanjin Jin, Yuan Liu +3
Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Observable Markov Decision Process (POMDP)…
Mimic Intent, Not Just Trajectories
Renming Huang, Chendong Zeng, Wenjing Tang +3
While imitation learning (IL) has achieved impressive success in dexterous manipulation through generative modeling and pretraining, state-of-the-art approaches like Vision-Languag…
Tru-POMDP: Task Planning Under Uncertainty via Tree of Hypotheses and Open-Ended POMDPs
Wenjing Tang, Xinyu He, Yongxi Huang +3
Task planning under uncertainty is essential for home-service robots operating in the real world. Tasks involve ambiguous human instructions, hidden or unknown object locations, an…
I-Perceive: A Foundation Model for Active Perception with Language Instructions
Yongxi Huang, Zhuohang Wang, Wenjing Tang +3
Active perception - the ability of a robot to proactively select viewpoints to acquire task-relevant information - is essential for robust operation in real-world environments. How…