3 papers
cs.RO2026
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)…
cs.LG2024
Goal-Reaching Policy Learning from Non-Expert Observations via Effective Subgoal Guidance
RenMing Huang, Shaochong Liu, Yunqiang Pei +4
In this work, we address the challenging problem of long-horizon goal-reaching policy learning from non-expert, action-free observation data. Unlike fully labeled expert data, our…
cs.LG2024
Diffusion Models as Optimizers for Efficient Planning in Offline RL
Renming Huang, Yunqiang Pei, Guoqing Wang +4
Diffusion models have shown strong competitiveness in offline reinforcement learning tasks by formulating decision-making as sequential generation. However, the practicality of the…