# RoboPrompt：利用稀疏人类输入进行直观机器人策略引导

> 原文链接：https://robotics.hellobike.com/papers/2026-10-07-2610-10534
> 频道：papers | 更新日期：2026-10-07 | 标签：arxiv, cs.RO
> 核心摘要：RoboPrompt将稀疏人类指引转为动作草稿，并由基础策略细化，在多种策略和任务上验证可提升成功率。

# RoboPrompt：利用稀疏人类输入进行直观机器人策略引导

::: v-pre
> 原文链接：[RoboPrompt: Intuitive Robot Policy Steering with Sparse Human Input](https://arxiv.org/abs/2610.10534)（arXiv 2610.10534 · 发布日期：2026-10-07）

**RoboPrompt: Intuitive Robot Policy Steering with Sparse Human Input**

RoboPrompt将稀疏人类指引转为动作草稿，并由基础策略细化，在多种策略和任务上验证可提升成功率。

## 核心要点

- 端到端模仿学习受数据多样性限制，可靠零样本真实部署仍具挑战。
- 方法将绘制轨迹、目标点和粗方向指令转为动作草稿，再由基础策略细化。
- RoboPrompt在噪声空间控制动作生成，无需修改基础策略架构或微调可引导性。
- 实验覆盖Diffusion Policy、π_{0.5}和FastWAM，并用DAgger迭代提升成功率、减少干预。

**验证范围与局限**：摘要未说明仿真或真机设置；验证范围为三种策略、三项任务及Insert Bread任务。

## 英文摘要 (Abstract)

End-to-end robot policies trained through imitation learning remain constrained by limited data diversity, making reliable zero-shot deployment in real-world settings challenging. Shared-autonomy methods enable human correction through teleoperation, but specialized hardware and operator training hinder deployment at scale. Other approaches incorporate human guidance as additional policy inputs, often requiring architectural changes and dedicated training for steerability, which limits their applicability across policies. We present RoboPrompt, a general-purpose, lightweight robot policy steering system that enables users to guide policy behavior through intuitive, sparse inputs, including drawn traces, target points, and coarse directional instructions. RoboPrompt decouples human-intention translation from the underlying policy: a reusable module converts human guidance into action drafts, which are refined through the diffusion or flow-matching dynamics of the base policy. By controlling action generation in noise space, RoboPrompt balances human intent with the policy prior without modifying the base policy architecture or fine-tuning it for steerability. Experiments demonstrate effective steering across Diffusion Policy, $π_{0.5}$, and FastWAM. We further use steered rollouts for online policy improvement through DAgger. After 2-3 rounds of iteration, average success rates increase by 15.5\% for $π_{0.5}$ across three tasks and by 21.3\% across three policies(Diffusion Policy, $π_{0.5}$, FastWAM) on the Insert Bread task, while average human intervention counts decrease by 44.0\% (2.86 to 1.60) and 81.9\% (2.60 to 0.47), respectively.

作者：Yanwen Zou, Chenyang Shi, Guoxuan Xu, Wenye Yu, Wendi Chen, Ye Pan, Cewu Lu, Chuan Wen
:::

## 原始参考来源 (Sources)

- [arXiv 2610.10534：RoboPrompt: Intuitive Robot Policy Steering with Sparse Human Input](https://arxiv.org/abs/2610.10534)