Publikation
Rapid Embodiment Adaptation for Quadrupedal Locomotion
Dichen Li; Bo Ai; Nico Bohlinger; Jan Peters; Hao Su; Henrik I. Christensen
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2608.01506, Pages 1-8, arXiv, 2026.
Zusammenfassung
Humans readily adapt their movements as their
bodies change through aging, injury, or load carrying, but
learning-based robot policies often break when hardware prop-
erties shift. We introduce an online embodiment adaptation
framework for quadrupedal locomotion that infers embodiment
parameters from short interaction histories and conditions
control on the inferred hardware state. Our method pairs a
generalist policy trained under embodiment randomization with
a lightweight adaptation module that identifies physical changes
within half a second. We evaluate two representative forms
of embodiment variation: joint-range constraints and trunk-
mass changes, corresponding to joint-level kinematic degra-
dation and body-level dynamic variation. In simulation, the
module accurately estimates these changes and enables closed-
loop control that substantially outperforms policies conditioned
directly on interaction history. On a real Unitree Go2 robot,
our system maintains stable locomotion under severe instances
of the evaluated changes, including a fully locked leg and a
5 kg payload, where non-adaptive methods fail. These results
demonstrate the practicality of explicit online embodiment
identification for rapid adaptation to joint-limit and payload-
mass changes, and provide a step toward handling broader
forms of uncertain, degraded, or changing robot hardware.