AI

RobotWorld: Benchmarking Multimodal Agents for Robot Use Across Diverse Tasks and Embodiments

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 03:18:24 UTC.

Briefings

RobotWorld is a simulation testbed for evaluating multimodal agents in physical tasks, featuring 84 tasks across manipulation, locomotion, and aerial control. Analysis reveals that while agents can build sophisticated perception and control workflows, they fail to consistently execute physical goals due to issues like losing object states or failing to recover from ineffective actions. Performance varies by model, with Astra excelling in spatial goals and Opus 5.5 in timed-interaction tasks, highlighting significant capability gaps between digital and physical execution.

Key points

• RobotWorld features 84 tasks covering manipulation, mobile manipulation, locomotion, driving, and aerial control with explicit interaction budgets.
• Agents consistently lose task-relevant object states despite reaching commanded poses, fail to correct ineffective actions, or mistake unfinished tasks for completion.

Context

This research investigates how general-purpose agents' digital capabilities transfer to the physical world, using RobotWorld as a rigorous proving ground to identify capability gaps.

Limitations

Evidence is limited to the simulation testbed; specific physical robot models are not named, and results are based on simulation outcomes rather than real-world performance.

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