AI

RoboQuest: Generalist Physical Agents that Search, Inspect and Test

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Briefings

RoboQuest is a benchmark for goal-directed embodied exploration using multimodal agents, featuring ten tasks with three uncertainty types. Five frontier agents were evaluated, with only 23% success rate and near-zero success for the fine-tuned policy. Most failures stem from premature exploration rather than execution skills, and trial-and-error learning remains difficult for most models.

Key points

• RoboQuest benchmark includes ten mobile manipulation tasks centered on search, manipulation-based inspection, and interactive testing.
• Five frontier multimodal agents were evaluated using a common visuomotor interface with a \u03c0_{0.5} policy fine-tuned on full-episode demonstrations.

Context

Recent advances in multimodal foundation models enable generalist physical agents for manipulation tasks. Agents must actively acquire task-relevant information through physical interaction when information is absent from observations, requiring autonomous decision-making on when to commit to task completion.

Limitations

Evidence is limited to five evaluated agents and ten tasks; failure analysis attributes only a minority of failures to execution skills. Results are preprint status with uncertainty regarding generalizability across different agent architectures.

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