6 Sol or GPT-6 Astra on 28 environments drawn from KinDER and PDDLStream.
Coding agents synthesize robot planners that generalize to unseen instances
Programs developed through simulator interaction surpassed hand-engineered planners while requiring substantially less computation at evaluation time.
Top University
Matteo Merler · Bowen Li · Josh Roy · Yichao Liang · Qianwei Wang · Yixuan Huang · +1 more
Fondazione Bruno Kessler · Carnegie Mellon University · Princeton University · University of Cambridge
Research Digest··2 min read
The authors gave coding agents task descriptions and simulator access, then asked them to develop reusable programs for 28 simulated task-and-motion planning environments.
Why this paper
From Carnegie Mellon University and 3 others · Part of World Model Planning for Agents, now 19 papers
In one line
Coding agents synthesize programs that generalize across task and motion planning instances, outperforming hand-engineered planners.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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