MorphAdapt: Action-Conditioned Temporal Adaptation across Unseen Robot Morphologies and Altered Dynamics

Shared robot control faces context variation at two timescales: embodiment changes across robots, while interaction dynamics can change within deployment. MorphAdapt assigns these information roles to a static morphology pathway and a temporal adaptation pathway. A morphology-conditioned shared controller consumes context inferred by a temporal Student from recent observations and previous actions; privileged dynamics supervise training but are unavailable at deployment. We evaluate this system jointly across unseen embodiments and altered dynamics on 97 held-out UNIMAL robots spanning 87 exact-XML morphology clusters. MorphAdapt achieves 9.3%–16.4% higher mean episodic return than MetaMorph-DR in four dynamics-mutation conditions, together with lower point estimates of maximum normalized velocity drop and recovered-only recovery time. MetaMorph-DR provides a system baseline without online temporal adaptation; a matched State-only Student separately tests the value of explicit action history. Previous-action conditioning yields substantial post-perturbation point gains in an individual development realization, while its incremental effect remains heterogeneous in the matched input ablation. The results support the complete two-timescale system on this benchmark and identify reliable use of action history across training realizations as an open learning problem.