aiqbench analysis
This paper introduces HATCH, a novel benchmark for robot manipulation that addresses the critical issue of unverifiable capability claims by utilizing hardware-attested task chains and sensor-based scoring. By replacing subjective human judgment with instrumentation and randomized physical setups, it establishes a reproducible standard for evaluating long-horizon robotic skills without relying on simulations or closed leaderboards. This approach directly advances the field of robotics by ensuring that performance metrics are robust against gaming and rehearsal, setting a new precedent for open evaluation in embodied AI.
HATCH: Hardware-Attested Task Chains for Verifiable Manipulation
arXiv:2610.27485v1 Announce Type: new Abstract: Capability claims in robot manipulation are unverifiable, and existing leaderboards are each closed, simulated, or gameable. We propose HATCH, a robot escape room: an openly specified, physically replicable, sensor-scored, and unrehearsable benchmark. Tasks span five skill categories at three difficulty levels and are chained so that the sensor confirming one task's completion unlocks the next, turning a room into a single long-horizon evaluation. Success is confirmed by instrumentation rather than human judgment, scoring is embodiment-agnostic, and object placements are randomized by a run-day seed. Entrants submit attested self-runs (uncut video, sensor logs, and the seed), with occasional organized events for calibration. Task specifications, build guide, bill of materials, and scoring rubrics are released under an open license.
Abstract:Capability claims in robot manipulation are unverifiable, and existing leaderboards are each closed, simulated, or gameable. We propose HATCH, a robot escape room: an openly specified, physically replicable, sensor-scored, and unrehearsable benchmark. Tasks span five skill categories at three difficulty levels and are chained so that the sensor confirming one task's completion unlocks the next, turning a room into a single long-horizon evaluation. Success is confirmed by instrumentation rather than human judgment, scoring is embodiment-agnostic, and object placements are randomized by a run-day seed. Entrants submit attested self-runs (uncut video, sensor logs, and the seed), with occasional organized events for calibration. Task specifications, build guide, bill of materials, and scoring rubrics are released under an open license.
2 pages, 1 table. Accepted at the 5th Workshop on Open Robotics Software, IROS 2026, Pittsburgh, PA, USA. Benchmark repository: this https URL
Robotics (cs.RO)
arXiv:2610.27485 [cs.RO]
(or arXiv:2610.27485v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.27485 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 233 words