Robot policy evaluation often relies on a binary success signal for task completion. In real-world deployment, however, desired execution behaviors are also defined by quality attributes such as execu
Quality-Controllable Policy enables real-time adjustment of robot execution quality and improves performance through iterative hindsight relabeling of online rollouts.
Robot policy evaluation often relies on a binary success signal for task completion. In real-world deployment, however, desired execution behaviors are also defined by quality attributes such as execution speed and motion smoothness, which need to be dynamically controlled according to task requirements. To address this, we introduce Quality-Controllable Policy (QCP), a robot policy that enables inference-time control over both individual quality attributes and their combinations. To extend the controllable quality range beyond initial demonstrations, we further propose an iterative hindsight relabeling loop: QCP collects online rollouts under target quality labels, adds successful…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Learning Quality-Controllable Robot Policies with Iterative Hindsight Relabeling was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 14 checks on 2 different days. Its best position was #22 of 30, reached on Nov 3, 11:28 UTC. It dropped off the list after Nov 3, 13:28 UTC.
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#22 on Hugging Face · Daily papers, on Nov 3, 11:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 2, 2026 and has been on the list on 2 days (14 checks) since.