Deploying Physically and Epistemically Compliant Agents by Distilling Belief-Space Task and Motion Planners
Overview
This paper presents a method that combines offline belief-space planning with policy distillation to create agents that act under uncertainty according to specified, belief-dependent rules. The authors address challenges in history-based policies through Dual Averaging loss and ρ-Optimal Reinforcement feedback. They introduce a new domain, Scavenger, where agents must gather information to succeed, evaluating the distilled policy on both visual and natural language command inputs. Results show the learned policy achieves over 90% success in Scavenger versus at most 5% for standard reinforcement learning, with strong transfer capabilities to unseen commands and larger floorplans.
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Reported quantity · Reported success rate: 90 percent · Basis not reported
“lanning implies this condition in static search with continuous beliefs. We create a challenging new domain, Scavenger, where an agent needs to gain information in order to succeed. We evaluate our distilled policy in two versions of Scavenger, one where information is provided visually and one where it is provided as a command in natural language. In either case, the learned policy attains a success rate above 90% i”
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- arXiv Robotics — research abstractsSignalDeploying Physically and Epistemically Compliant Agents by Distilling Belief-Space Task and Motion Planners
This paper presents a method that combines offline belief-space planning with policy distillation to create agents that act under uncertainty according to specified, belief-dependent rules. The authors address challenges in history-based policies through Dual Averaging loss and ρ-Optimal Reinforcement feedback. They introduce a new domain, Scavenger, where agents must gather information to succeed, evaluating the distilled policy on both visual and natural language command inputs. Results show the learned policy achieves over 90% success in Scavenger versus at most 5% for standard reinforcement learning, with strong transfer capabilities to unseen commands and larger floorplans.
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