# EmbodiedSWE：用编码智能体为长时程灵巧机器人任务提供可扩展监督

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-23 08:00
- AIHOT 分数：46
- AIHOT 链接：https://aihot.news/items/cmuf32nqp08h3rood44avzy6x
- 原文链接：https://arxiv.org/abs/2609.27308

## AI 摘要

研究团队提出 EmbodiedSWE 框架，并构建 EMBODIEDSWE-BENCH 仿真基准，覆盖富接触操作、可变形物体及需长达半小时连续交互的长时程任务，发现前沿编码智能体可求解复杂长时程任务并跨任务、跨本体迁移已有解法。

## 正文

We study coding agents for long-horizon, dexterous robotics and ask whether their solutions can provide scalable supervision for learning general robot policies. To test this, we develop EMBODIEDSWE-BENCH, a simulation benchmark for coding agents spanning contact-rich manipulation, deformable objects, and long-horizon tasks requiring up to half an hour of continuous interaction. We find that frontier coding agents can solve complex long-horizon tasks and transfer prior solutions across both tasks and embodiments. We also design supporting tools that help agents more effectively solve these tasks.

However, the resulting solutions require substantial iterative interaction and are typically specialized to individual task instances. We therefore introduce EMBODIEDSWE-GEN, which expands a single solution from coding agent into large diverse trajectories for training a VLA. VLA performance improves with more generated demonstrations, and agent-aided diversification improves generalization to held-out task variations. We also show that a VLA finetuned solely on coding-agent-generated simulation demonstrations completes a long-horizon task on real robot.

Together, our framework uses coding agents to solve complex robotics tasks and turn verified solutions into scalable supervision for robot policies.