AgenticGenTAMP: coding agents synthesize reusable robot TAMP policies across 28 envs
tomssilver · x · 2026-09-25
Tom Silver's group at Princeton and collaborators present AgenticGenTAMP, using coding agents to synthesize reusable programmatic policies for generalized task and motion planning, evaluated across 28 simulated environments.
Key points:
- Generalized TAMP exploits regularities across instances (varying object counts, configurations, geometry) so one program covers them all, via learned samplers, feasibility predictors, search heuristics, or abstractions
- Strict setup: the agent gets only task descriptions and state/action spaces, a $20 model-usage budget per synthesis run, no network or host filesystem, isolated Docker execution, and no environment source code—just a client API
- Paper, code, and videos are public
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