Article

RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation

The authors propose RMMBench, a mobile manipulation benchmark that requires robots to understand language instructions and execute long-horizon tasks in continuous space, with a "navigation-manipulation" task suite covering 70 typical task scenarios. Results show that leading vision-language models (VLMs) still face notable challenges in spatial localization for mobile manipulation tasks, and their spatial perception in long-horizon interactions needs strengthening. The work has been accepted to CoRL 2026.

Part of the event: RMMBench Robot Mobile Manipulation Benchmark Released

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