pith. sign in

arxiv: 2203.17138 · v1 · pith:2DTUKZTWnew · submitted 2022-03-31 · 💻 cs.RO · cs.AI· cs.LG

Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors

Steven Bohez , Saran Tunyasuvunakool , Philemon Brakel , Fereshteh Sadeghi , Leonard Hasenclever , Yuval Tassa , Emilio Parisotto , Jan Humplik
show 13 more authors
Tuomas Haarnoja Roland Hafner Markus Wulfmeier Michael Neunert Ben Moran Noah Siegel Andrea Huber Francesco Romano Nathan Batchelor Federico Casarini Josh Merel Raia Hadsell Nicolas Heess
This is my paper
classification 💻 cs.RO cs.AIcs.LG
keywords humanmodulemovementskillanimalapproachdatalearn
0
0 comments X
read the original abstract

We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots. Our approach builds upon previous work on imitating human or dog Motion Capture (MoCap) data to learn a movement skill module. Once learned, this skill module can be reused for complex downstream tasks. Importantly, due to the prior imposed by the MoCap data, our approach does not require extensive reward engineering to produce sensible and natural looking behavior at the time of reuse. This makes it easy to create well-regularized, task-oriented controllers that are suitable for deployment on real robots. We demonstrate how our skill module can be used for imitation, and train controllable walking and ball dribbling policies for both the ANYmal quadruped and OP3 humanoid. These policies are then deployed on hardware via zero-shot simulation-to-reality transfer. Accompanying videos are available at https://bit.ly/robot-npmp.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Unleashing Infinite Motion: Scaling Expressive Quadrupedal Motion via Generative Video Priors

    cs.RO 2026-06 conditional novelty 7.0

    Uni-Mo generates 7,488 language-annotated quadruped motions via LLM prompts and video diffusion, lifts them to 3D trajectories, and trains policies achieving 96.7% real-robot success on 392 sampled motions.

  2. MotionPyramid: Hierarchical Motion Representation and Residual Interfaces

    cs.CV 2026-06 unverdicted novelty 6.0

    MotionPyramid learns a stack of latent decoders from motion tracking data to create multi-resolution action interfaces for RL policies in humanoid control, with residual interfaces allowing coarse programs and fine co...

  3. The embodied brain: Bridging the brain, body, and behavior with neuromechanical digital twins

    q-bio.NC 2026-01 unverdicted novelty 2.0

    Neuromechanical digital twins embed neural controllers in simulated bodies to infer unmeasurable biophysical variables, generate testable hypotheses via perturbations, and bridge neuroscience with robotics and machine...