aiqbench analysis
This research investigates the efficacy of failure-specific feedback versus alternative strategies like resampling and static guidance in enabling code-as-policy agents to self-repair within simulated robotic manipulation tasks. The study demonstrates that while specific feedback templates offer advantages over simple resampling, they can also introduce incompatibilities if the instructions do not align with the physical constraints of the task, such as grasp requirements. Evaluations for autonomous agent systems should therefore include these diverse baselines to accurately assess recovery mechanisms under fixed computational budgets.
Rethinking Self-Repair in Code-as-Policy Agents: What Drives Retry Success?
arXiv:2611.06863v1 Announce Type: new Abstract: Code-as-policy agents often use execution feedback to retry failed programs. Additional sampling and task guidance can also improve success, leaving the contribution of failure-specific feedback unclear. We study this question in simulated robot manipulation. A fixed lookup selects feedback templates using failure labels and the API variant. A frozen code model generates a fresh program after each scene reset. On the same 252 initial failures, Qwen2.5-Coder-7B recovers 85.3% within five retries, compared with 68.3% for resampling with the same retry cap. The advantage persists under fixed total-token budgets. Feedback from other episodes and fixed task guidance yield similar recovery point estimates, with no detected benefit from matching the coarse failure label. In a separate study, a fixed guided prompt used from the first attempt achieves an observed overall success rate similar to that of template-based feedback. Template-based feedback also lowers recovery on a nut-insertion task requiring a rim grasp, consistent with an incompatible center-grasp instruction. Evaluations of robot self-repair should include resampling, mismatched feedback, and concise, task-compatible static guidance as baselines.
Title:Rethinking Self-Repair in Code-as-Policy Agents: What Drives Retry Success?
Abstract:Code-as-policy agents often use execution feedback to retry failed programs. Additional sampling and task guidance can also improve success, leaving the contribution of failure-specific feedback unclear. We study this question in simulated robot manipulation. A fixed lookup selects feedback templates using failure labels and the API variant. A frozen code model generates a fresh program after each scene reset. On the same 252 initial failures, Qwen2.5-Coder-7B recovers 85.3% within five retries, compared with 68.3% for resampling with the same retry cap. The advantage persists under fixed total-token budgets. Feedback from other episodes and fixed task guidance yield similar recovery point estimates, with no detected benefit from matching the coarse failure label. In a separate study, a fixed guided prompt used from the first attempt achieves an observed overall success rate similar to that of template-based feedback. Template-based feedback also lowers recovery on a nut-insertion task requiring a rim grasp, consistent with an incompatible center-grasp instruction. Evaluations of robot self-repair should include resampling, mismatched feedback, and concise, task-compatible static guidance as baselines.
Robotics (cs.RO)
arXiv:2611.06863 [cs.RO]
(or arXiv:2611.06863v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.06863 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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