# RoboAware：从反事实结果中学习协调具身技能

> 原文链接：https://robotics.hellobike.com/papers/2026-10-08-2610-11480
> 频道：papers | 更新日期：2026-10-08 | 标签：arxiv, cs.RO
> 核心摘要：RoboAware针对具身技能组合中的策略族选择，借反事实分支结果学习状态条件协调器，并在100项单回合任务上验证。

# RoboAware：从反事实结果中学习协调具身技能

::: v-pre
> 原文链接：[RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes](https://arxiv.org/abs/2610.11480)（arXiv 2610.11480 · 发布日期：2026-10-08）

**RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes**

RoboAware针对具身技能组合中的策略族选择，借反事实分支结果学习状态条件协调器，并在100项单回合任务上验证。

## 核心要点

- 问题：模块化机器人技能可与冻结端到端策略组合，但需预判当前状态下哪类策略会成功。
- 方法：提出P^5模式与层级MDP，并用SCB在同一训练状态下生成各可用策略族的反事实结果。
- 学习：EAL结合蒙特卡洛树搜索与Q学习，将分支结果蒸馏为策略族条件化价值。
- 结果：在100项单回合评估中，总成功率77.0%，并优于code-as-policy和VLA-harness基线。

**验证范围与局限**：验证范围为100项单回合任务，涵盖RoboSuite、LIBERO-Pro任务簇和RoboTwin双臂任务。

## 英文摘要 (Abstract)

Embodied coding agents can combine modular robot skills with frozen end-to-end policies, yet effective composition requires anticipating which policy family will succeed in the current physical state. We present RoboAware, which builds on coding agents' skill orchestration by learning only a state-conditioned responsibility coordinator from counterfactual outcomes. Inspired by the success of REPL, we propose the $P^5$ schema and formulate a hierarchical MDP based on it. $P^5$ organizes skills uniformly into five semantic stages, defining where responsibility can be compared. To address the lack of counterfactual branch outcomes in existing work, we introduce State-Locked Counterfactual Branching (SCB), which restores the same training state to generate and execute a code block from each admissible family, exposing outcomes that selected-branch experience leaves unobserved. Building on this, we propose Execution-Aware Learning (EAL), which combines Monte Carlo tree search with Q-learning to distill these outcomes into family-conditioned values. At deployment, the coordinator selects the policy family according to observable context, and the frozen coding agent generates the next local code block. Comprehensive single-episode evaluations on 100 tasks show that RoboAware reaches a 77.0% overall success rate, with SOTA averages of 90.0% on RoboSuite, 73.8% on diverse LIBERO-Pro task clusters, and 90.0% on challenging RoboTwin bimanual tasks, outperforming existing code-as-policy and VLA-harness baselines.

作者：Bohan Zhou, Xingbei Chen, Emily Huang, Weilin Ruan, Haojian Huang, Y. Zhang, Zexi Li, Wenqian Li, Qize Yu, Zetian Song
:::

## 原始参考来源 (Sources)

- [arXiv 2610.11480：RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes](https://arxiv.org/abs/2610.11480)