arXiv cs.AI· So Kuroki, Kento Kawaharazuka, Yujin Tang·· 14 小时前AI 评分35
学习与迁移闭环机器人软件
Learning and Transferring Closed-Loop Robot Software
AI 导读
研究将完整闭环机器人实现视为可复用执行经验：编码智能体从少量成功演示生成策略代码，并借助仿真反馈迭代优化，验证筛选后的实现存入软件档案。在 RoboCasa 四个源任务上，成功率从初始生成代码的 28.3% 提升至验证筛选代码的 64.2%，并在 Unitree G1 实体机器人上验证。在九个迁移目标任务上，无参考为 45.2%，使用源任务初始代码为 41.5%，使用验证筛选代码达 57.0%。
正文 · 原文
Abstract:Closed-loop robot policies require observation processing, state management, and situation-dependent branching, making them costly to design and tune manually. Although coding agents increasingly support control-code generation and optimization, it remains unclear whether implementations improved on source tasks also support policy acquisition for new tasks. We study this question by treating complete closed-loop implementations as reusable execution experience. To learn each source policy, a coding agent generates policy code from a few successful demonstrations and iteratively improves it using simulation feedback. The validation-selected implementations are retained in a software archive. To transfer the acquired software to new tasks, the agent generates and improves policies using archived implementations, target demonstrations, and execution feedback. We evaluate policy learning and its relationship to cross-task transfer. For learning, across four source tasks in RoboCasa, iterative optimization increases the success rate from 28.3% with initially generated policy code to 64.2% with validation-selected policy code. We further validate this learning approach on a physical Unitree G1. For transfer, across nine additional target tasks in RoboCasa, success rates are 45.2% without references, 41.5% using initially generated policy code from source tasks, and 57.0% using their validation-selected policy code. These results show that improving source-task code also improves learning on new tasks.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.19906 [cs.RO] |
| (or arXiv:2609.19906v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2609.19906 arXiv-issued DOI via DataCite |
Submission history
From: So Kuroki [view email]
[v1]
Thu, 17 Sep 2026 08:51:12 UTC (1,237 KB)
[v2]
Thu, 29 Oct 2026 10:18:41 UTC (3,039 KB)
来源：arXiv cs.AI · arxiv.org