7 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)…
UniDomain: Pretraining a Unified PDDL Domain from Real-World Demonstrations for Generalizable Robot Task Planning
Haoming Ye, Yunxiao Xiao, Cewu Lu +1
Robotic task planning in real-world environments requires reasoning over implicit constraints from language and vision. While LLMs and VLMs offer strong priors, they struggle with…
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…
Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks
Zhihong Liu, Yang Li, Rengming Huang +2
Open world language conditioned task planning is crucial for robots operating in large-scale household environments. While many recent works attempt to address this problem using L…
UniPlan: Vision-Language Task Planning for Mobile Manipulation with Unified PDDL Formulation
Haoming Ye, Yunxiao Xiao, Cewu Lu +1
Integration of VLM reasoning with symbolic planning has proven to be a promising approach to real-world robot task planning. Existing work like UniDomain effectively learns symboli…