# Embodied Skill Bench：评测具备具身技能的智能体在物理任务上的表现

> 原文链接：https://robotics.hellobike.com/papers/2026-10-12-2610-16238
> 频道：papers | 更新日期：2026-10-12 | 标签：arxiv, cs.RO
> 核心摘要：该工作将多类模块封装为具身技能，提出EmbodiedSkillBench，并在四个智能体的100个扰动LIBERO-Pro任务上验证复用效果。

# Embodied Skill Bench：评测具备具身技能的智能体在物理任务上的表现

::: v-pre
> 原文链接：[Embodied Skill Bench: Benchmarking How Well Agents with Embodied Skills Perform on Physical Tasks](https://arxiv.org/abs/2610.16238)（arXiv 2610.16238 · 发布日期：2026-10-12）

**Embodied Skill Bench: Benchmarking How Well Agents with Embodied Skills Perform on Physical Tasks**

该工作将多类模块封装为具身技能，提出EmbodiedSkillBench，并在四个智能体的100个扰动LIBERO-Pro任务上验证复用效果。

## 核心要点

- VLA策略、感知模型和程序知识接口与具身假设不同，难以复用且收益不清。
- 作者构建250个动作、感知和经验技能，并提出含ADR的三种评测模式。
- 在100个扰动LIBERO-Pro任务上，技能可提升较弱智能体并减少token，但会限制强智能体。
- ADR让GPT-6-Astra保留原生控制并选择性用技能，达85%成功率且减少token和时间；代码已开源。

**验证范围与局限**：验证范围为四个智能体在100个扰动LIBERO-Pro任务上的闭环评测。

## 英文摘要 (Abstract)

LLM agents increasingly control robots using VLA policies, perception models, and procedural knowledge. These modules are difficult to reuse because their interfaces, embodiment assumptions, and conditions of use differ, and their benefits on physical tasks remain unclear. We package them as embodied skills that extend agent skills with explicit embodiment assumptions and conditions of use, building 250 action, perception, and experience skills from 1,052 resources. On this basis we introduce EmbodiedSkillBench, which runs an agent in a closed observation-action loop and records success, actions, tokens, and wall-clock time. It supports three modes: native control, skill-only execution, and Agent-Directed Reuse (ADR), a mode we propose in which the agent keeps its native controls and decides for each skill whether to invoke, modify, or ignore it. Across four agents on 100 perturbed LIBERO-Pro tasks, we find that skills help an agent only as far as they exceed what it can already do. Skill-only execution lifts GPT-5.6-Sol from 11% to 63% and Kimi-K3 and Claude-Opus-5 by about 20 points, while cutting their token usage by 58% to 85%. Yet all four agents then end up between 61% and 64%, and GPT-6-Astra falls from 92% under native control to 61%. This suggests that as agents grow stronger, the role of embodied skills shifts from supplying missing capability to reducing cost: GPT-6-Astra already solves most tasks through native control, so restricting it to skills removes more than it adds. Under ADR, which lets it keep native control and call skills selectively, GPT-6-Astra recovers to 85%, within 7 points of native control, while using 14.1% fewer tokens and 11.9% less time. With Jev as a skill choosing motion directions and gripper actions, it reaches 81% with 50% fewer tokens and 57% less time. The code is available at https://github.com/HanZhi306/EmbodiedSkill-Bench.

作者：Zhi Han, Zihan Guo, Chenxi Zeng, Zicai Cui, Yue Xin, Ming Zhou, Dongzhan Zhou, Weinan Zhang, Yang Li
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## 原始参考来源 (Sources)

- [arXiv 2610.16238：Embodied Skill Bench: Benchmarking How Well Agents with Embodied Skills Perform on Physical Tasks](https://arxiv.org/abs/2610.16238)