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 Reset, which restores the scene by planning over a library of learned atomic reset skills, so de...
| Authors | Jing Jiang, Yue Yang, Xinkai Jiang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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