BodyBound: A Benchmark for Diagnosing Robot-Policy Responses to Body Changes Beyond Task Success

Task success does not by itself show whether a robot policy adapts when its body changes. A policy may fail before the intervention, or the same action sequence may work on both bodies. BodyBound is a controlled benchmark for separating these cases. It pairs executions with the same scene, goal, initial state, and rollout budget while changing one body property: joint limits, jaw capacity, finger-force limit, or wrist-guard geometry within a Panda topology. An independent trace checker is combined with four diagnostics: familiar-start competence, expert attainability witnesses, reciprocal body-description interventions, and fixed-action replay. We release 100 simulated RGB-D demonstrations and evaluate HPT and Diffusion Policy on 60 fresh paired scenes (720 matching-description rollouts). On the public test split, success is 4.2% for HPT and 19.4% for Diffusion Policy; all six checkpoints obtain zero successes in Scene OOD. The learned policies also fail on many familiar starts, limiting attribution of failures to body changes. In a competent Portal reference, all 16 fresh pairs succeed with zero description benefit and shared successful actions; a Grasp reference shows a positive description benefit despite shared solutions. BodyBound therefore measures competence, physical response, and evidence of adaptation separately.