Correct Robots Before They Make Mistakes: Proactive Human-in-the-Loop Intervention via Preference Learning
Overview
This paper introduces PHIL, a framework enabling operators to preview and intervene on robot policy predictions before undesirable actions occur. Using an XR interface, PHIL displays predicted trajectories during takeover and pairs rejected predictions with corrective action chunks for preference-based post-training. Evaluated on four real-world manipulation tasks with five correction episodes per condition, PHIL increases mean success rates from 44.0% to 70.3% for diffusion policy and from 57.8% to 76.9% for X-VLA compared to HG-DAgger, while reducing unnecessary recovery behaviors in successful episodes.
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“arXiv:2611.05667v1 Announce Type: new Abstract: 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 workspa”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 44 percent · reported trials · Differing source assertions
“e 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/.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 70.3 percent · reported trials · Differing source assertions
“e 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/.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 57.8 percent · reported trials · Differing source assertions
“e 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/.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 76.9 percent · reported trials · Differing source assertions
“e 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/.”
Exact source · revision 1Source owner not reported
Reported quantity · number of tasks: 4 other · Basis not reported
“ce 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 fiv”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalCorrect Robots Before They Make Mistakes: Proactive Human-in-the-Loop Intervention via Preference Learning
This paper introduces PHIL, a framework enabling operators to preview and intervene on robot policy predictions before undesirable actions occur. Using an XR interface, PHIL displays predicted trajectories during takeover and pairs rejected predictions with corrective action chunks for preference-based post-training. Evaluated on four real-world manipulation tasks with five correction episodes per condition, PHIL increases mean success rates from 44.0% to 70.3% for diffusion policy and from 57.8% to 76.9% for X-VLA compared to HG-DAgger, while reducing unnecessary recovery behaviors in successful episodes.
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