MolmoSpaces-CommonSense: Evaluating Commonsense in Robotic Manipulation Policies
Overview
This paper introduces MolmoSpaces-CommonSense, a benchmark for evaluating commonsense reasoning in robotic manipulation policies. The authors address limitations in existing simulation benchmarks by incorporating tasks requiring reasoning about occlusion, containment, support relations, and hidden objects. Evaluations of several zero-shot generalist manipulation policies reveal consistent failures: success rates drop by over 50% as distractors increase, policies act on incorrect objects 30% of the time in cluttered settings, and affordance-sensitive grasping remains unreliable with only about 50% correct object part selection even with specific language prompts. The study suggests existing benchmarks may overestimate zero-shot real-world readiness.
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Reported quantity · Reported success rate: 50 percent · Basis not reported · Reported zero-shot result; the excerpt does not specify an evaluation protocol.
“ffordances, and hidden objects. These evaluations require policies not only to recognize relevant scene structure but also to execute correct actions in them. We evaluate several zero-shot generalist manipulation policies. Across model types and architectures, we observe consistent failures in commonsense reasoning: success rates in cluttered scenes drop by over 50% as distractors increase, policies act on incorrect”
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“results suggest that existing benchmarks may substantially overestimate zero-shot real-world readiness. Code and policy leaderboard link at https://github.com/allenai/molmospaces.”
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- arXiv Robotics — research abstractsSignalMolmoSpaces-CommonSense: Evaluating Commonsense in Robotic Manipulation Policies
This paper introduces MolmoSpaces-CommonSense, a benchmark for evaluating commonsense reasoning in robotic manipulation policies. The authors address limitations in existing simulation benchmarks by incorporating tasks requiring reasoning about occlusion, containment, support relations, and hidden objects. Evaluations of several zero-shot generalist manipulation policies reveal consistent failures: success rates drop by over 50% as distractors increase, policies act on incorrect objects 30% of the time in cluttered settings, and affordance-sensitive grasping remains unreliable with only about 50% correct object part selection even with specific language prompts. The study suggests existing benchmarks may overestimate zero-shot real-world readiness.
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