Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch

Hudson, Eddy; Warnell, Garrett; Torabi, Faraz; Stone, Peter

doi:10.1109/icra46639.2022.9812127

Cited by 4 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work by Desai et al (2020) discusses the imitation transfer problem between different domains and presents an action transformation method for the state-only imitation setting. Hudson et al (2022) on the other hand learn an affine transform to compensate for differences in the skeletons of the demonstrator and the imitator. These methods are based on transforming either actions or states to a comparable representation.…”

Section: Related Workmentioning

confidence: 99%

Co-imitation: Learning Design and Behaviour by Imitation

Rajani

Arndt

Blanco-Mulero

et al. 2023

AAAI

View full text Add to dashboard Cite

The co-adaptation of robots has been a long-standing research endeavour with the goal of adapting both body and behaviour of a robot for a given task, inspired by the natural evolution of animals. Co-adaptation has the potential to eliminate costly manual hardware engineering as well as improve the performance of systems. The standard approach to co-adaptation is to use a reward function for optimizing behaviour and morphology. However, defining and constructing such reward functions is notoriously difficult and often a significant engineering effort. This paper introduces a new viewpoint on the co-adaptation problem, which we call co-imitation: finding a morphology and a policy that allow an imitator to closely match the behaviour of a demonstrator. To this end we propose a co-imitation methodology for adapting behaviour and morphology by matching state-distributions of the demonstrator. Specifically, we focus on the challenging scenario with mismatched state- and action-spaces between both agents. We find that co-imitation increases behaviour similarity across a variety of tasks and settings, and demonstrate co-imitation by transferring human walking, jogging and kicking skills onto a simulated humanoid.

show abstract

Section: Related Workmentioning

confidence: 99%