← 研踪 PaperTrailarXiv:2611.05667 · v1
Correct Robots Before They Make Mistakes: Proactive Human-in-the-Loop Intervention via Preference Learning
首次提交 2026-11-04 05:54
版本更新 2026-11-04 05:54
雷达观测 2026-11-05 10:08
摘要
This paper presents Proactive Human-in-the-Loop (PHIL), a framework for proactive policy correction and preference-based post-training. PHIL enables operators to preview policy-predicted future motion and intervene before anticipated undesirable actions. During takeover, PHIL continues querying the policy and displaying its predicted trajectories in the physical workspace through an extended reality (XR) interface. PHIL pairs rejected policy predictions with corrective action chunks subsequently executed from the same physical state over the same horizon, and preference-based post-training encourages the policy to favor these corrections. This paper evaluates PHIL on four real-world manipulation tasks using a diffusion policy and a pretrained vision-language-action policy. With five correction episodes per condition, PHIL increases mean success rates across the evaluated tasks and demonstration budgets from 44.0% to 70.3% for the diffusion policy and from 57.8% to 76.9% for X-VLA, compared with HG-DAgger. Separately, policies trained with proactive corrections show fewer unnecessary recovery behaviors in successful episodes. Project page: https://intuitive-robots.github.io/PHIL-page/.
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v1 · 2026-11-04 05:54 · 最新观测版本
Correct Robots Before They Make Mistakes: Proactive Human-in-the-Loop Intervention via Preference Learning
This paper presents Proactive Human-in-the-Loop (PHIL), a framework for proactive policy correction and preference-based post-training. PHIL enables operators to preview policy-predicted future motion and intervene before anticipated undesirable actions. During takeover, PHIL continues querying the policy and displaying its predicted trajectories in the physical workspace through an extended reality (XR) interface. PHIL pairs rejected policy predictions with corrective action chunks subsequently executed from the same physical state over the same horizon, and preference-based post-training encourages the policy to favor these corrections. This paper evaluates PHIL on four real-world manipulation tasks using a diffusion policy and a pretrained vision-language-action policy. With five correction episodes per condition, PHIL increases mean success rates across the evaluated tasks and demonstration budgets from 44.0% to 70.3% for the diffusion policy and from 57.8% to 76.9% for X-VLA, compared with HG-DAgger. Separately, policies trained with proactive corrections show fewer unnecessary recovery behaviors in successful episodes. Project page: https://intuitive-robots.github.io/PHIL-page/.
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