Princeton-led work uses coding agents to synthesize reusable policies for generalized task and motion planning
tomssilver · x · 2026-09-25
A Princeton team led by Tom Silver, with CMU, Cambridge and FBK collaborators, released AgenticGenTAMP: a study evaluating whether coding agents can synthesize a single reusable programmatic policy for generalized task and motion planning (TAMP) across 28 simulated environments.
- Setup: In TAMP, discrete choices (which object, which tool, which subgoal) couple with continuous geometric, kinematic and dynamic constraints. Within an environment, instances differ in object count, configuration and geometry but share state/action spaces and rewards — the agent must generalize.
- Restrictions: The agent gets only the task description, observation/action spaces, and goal; each synthesis run has a $20 model-usage budget and may choose its own simulator tests. No network or host filesystem access; code runs in an isolated Docker container; no environment source code, only a client API.
- States are fully observed and object-centric; perception and language understanding are out of scope. Paper, code, full results and rollout gallery are on the project site.
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