aiqbench analysis
The paper introduces a benchmark for a narrower problem than most embodied-AI evaluations: detecting when a human's household task execution goes wrong, rather than inferring goals or confirming completion. Its finding that learned, symbolic, change-point, and frontier-LLM baselines all miss errors suggests current methods are weak at this kind of monitoring. For readers, the main value is the neuropsychology-grounded error taxonomy and the streaming evaluation setup, which could matter for assistive-agent research.
HelpMeOut: A Neuropsychology-Grounded Benchmark for Error-Aware Monitoring of Household Task Execution
arXiv:2610.17694v1 Announce Type: cross Abstract: We introduce HelpMeOut, an embodied AI benchmark for error-aware monitoring of household task execution. Existing embodied household benchmarks typically assume that humans execute tasks correctly, but this assumption does not hold for cognitively impaired persons or older adults whose everyday activities produce structured execution errors. To address this gap, HelpMeOut operationalizes research on everyday action errors into 20000 paired activity episodes, covering nine neuropsychologically motivated error categories. In each episode, agents observe a streaming human activity trajectory and must monitor whether the execution is unfolding correctly, diagnosing any structured errors as early as possible. We evaluate learned-monitor, symbolic, change-point, and frontier LLM prompting baselines. Results show that these methods perform poorly as error monitors, often missing when execution goes wrong. These results suggest a gap left by standard goal-inference and task-completion benchmarks, making HelpMeOut a testbed for error-aware assistive agents.
Abstract:We introduce HelpMeOut, an embodied AI benchmark for error-aware monitoring of household task execution. Existing embodied household benchmarks typically assume that humans execute tasks correctly, but this assumption does not hold for cognitively impaired persons or older adults whose everyday activities produce structured execution errors. To address this gap, HelpMeOut operationalizes research on everyday action errors into 20000 paired activity episodes, covering nine neuropsychologically motivated error categories. In each episode, agents observe a streaming human activity trajectory and must monitor whether the execution is unfolding correctly, diagnosing any structured errors as early as possible. We evaluate learned-monitor, symbolic, change-point, and frontier LLM prompting baselines. Results show that these methods perform poorly as error monitors, often missing when execution goes wrong. These results suggest a gap left by standard goal-inference and task-completion benchmarks, making HelpMeOut a testbed for error-aware assistive agents.
Accepted to the 10th Conference on Robot Learning (CoRL 2026). 30 pages (9-page main text), 8 figures, 18 tables. Project page: this https URL
Robotics (cs.RO); Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
arXiv:2610.17694 [cs.RO]
(or arXiv:2610.17694v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.17694 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 263 words
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