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From Rollout to Reset: A Graph-Based Harness for Autonomous Long-Horizon Manipulation Evaluation
Robot manipulation policies are improving quickly, and real-robot evaluation remains the standard evidence for that progress. It still relies on a human to reset the scene between rollouts, which consumes operator time and leaves the initial state distribution unspecified, so results reproduce poorly. A recent system, AutoEval, automates both reset and scoring, but only for single-step tasks, because a long-horizon rollout can terminate in combinatorially many configurations that no single learned reset policy covers. We present HALTER, a Harness for Autonomous Long-horizon Task Evaluation and
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T20:52:17.000Z
- arXiv · Artificial Intelligence · 2026-09-16T20:52:17.000Z
First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.