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Spatio-Temporal Motion Retargeting for Quadruped Robots (2404.11557v2)

Published 17 Apr 2024 in cs.RO

Abstract: This work introduces a motion retargeting approach for legged robots, which aims to create motion controllers that imitate the fine behavior of animals. Our approach, namely spatio-temporal motion retargeting (STMR), guides imitation learning procedures by transferring motion from source to target, effectively bridging the morphological disparities by ensuring the feasibility of imitation on the target system. Our STMR method comprises two components: spatial motion retargeting (SMR) and temporal motion retargeting (TMR). On the one hand, SMR tackles motion retargeting at the kinematic level by generating kinematically feasible whole-body motions from keypoint trajectories. On the other hand, TMR aims to retarget motion at the dynamic level by optimizing motion in the temporal domain. We showcase the effectiveness of our method in facilitating Imitation Learning (IL) for complex animal movements through a series of simulation and hardware experiments. In these experiments, our STMR method successfully tailored complex animal motions from various media, including video captured by a hand-held camera, to fit the morphology and physical properties of the target robots. This enabled RL policy training for precise motion tracking, while baseline methods struggled with highly dynamic motion involving flying phases. Moreover, we validated that the control policy can successfully imitate six different motions in two quadruped robots with different dimensions and physical properties in real-world settings.

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Authors (7)
  1. Taerim Yoon (2 papers)
  2. Dongho Kang (7 papers)
  3. Seungmin Kim (4 papers)
  4. Minsung Ahn (1 paper)
  5. Stelian Coros (50 papers)
  6. Sungjoon Choi (33 papers)
  7. Jin Cheng (32 papers)
Citations (3)
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Summary

  • The paper introduces the STMR method that decouples spatial and temporal retargeting for transferring animal movement data onto quadruped robots.
  • It utilizes SMR to enforce kinematic constraints and TMR to adjust temporal parameters, eliminating artifacts like foot sliding.
  • Experiments demonstrate improved tracking performance over baseline methods, enabling quadrupeds to replicate complex animal behaviors robustly.

Spatio-Temporal Motion Retargeting for Quadruped Robots: An Expert Overview

The paper proposes a methodology for the retargeting of motion data onto quadruped robots, aimed at enhancing the fidelity and feasibility of transferred movements from animal behaviors to robotic systems. The method, referred to as Spatio-Temporal Motion Retargeting (STMR), is designed to bridge the morphological differences between source and target systems while ensuring that the generated motions are both kinematically and dynamically feasible.

Core Components and Approach

The proposed STMR consists of two main components: Spatial Motion Retargeting (SMR) and Temporal Motion Retargeting (TMR). Each component addresses different challenges associated with motion retargeting:

  1. Spatial Motion Retargeting (SMR): This component operates at the kinematic level. It focuses on eliminating kinematic artifacts such as foot sliding and penetration by imposing constraints on foot movement. SMR generates a full-body motion trajectory that adheres to the contact schedules of the original motion, ensuring that the essence and timing of the movements are maintained despite the change in morphology.
  2. Temporal Motion Retargeting (TMR): Working at the dynamic level, TMR optimizes the temporal parameters to ensure that the motion is dynamically feasible. It focuses on characteristics such as flight phases in jumping motions, which naturally require adjusted timing depending on the size and physical properties of the robot. The authors utilize a model-based control approach to score and iteratively refine the temporal parameters, assessing the feasibility and performance of the retargeted motion.

Numerical Results and Experiments

The paper details a series of simulation and real-world experiments to evaluate the efficacy of the STMR approach. The authors conducted extensive trials across six distinct motions and three different quadruped robotic configurations. Their method successfully preserved contact schedules and eliminated sliding artifacts, while enabling robots of various sizes to mimic six complex animal motions.

In the experiments, the authors compared the STMR-generated control policies to three baseline imitation learning methods. Tracking performance improvements were observed with significant reductions in L1 error distances, showcasing the advantage of using the STMR-generated kino-dynamically feasible reference motions. Furthermore, STMR facilitated the extraction and reconstruction of whole-body motion from videos captured with hand-held cameras, proving its capability to work with data lacking global body pose information.

Practical Implications and Future Directions

The development of STMR presents several practical benefits for the field of robotics. It offers a robust framework for adapting complex animal movements to quadruped robots, which is valuable for enhancing robotic capabilities in dynamic and interactive environments, such as search and rescue operations or assistance robots. The method also supports the integration of diverse motion data sources, enriching the repertoire of robotic skills and improving deployment versatility.

Theoretically, this work reinforces the importance of accounting for both spatial and temporal aspects in motion retargeting, demonstrating that a decoupled approach can yield feasible and expressive robotic movements. Future research directions could explore the generalization of STMR to other legged robotic morphologies, such as humanoids, and enhance robustness against environmental uncertainties and modeling discrepancies. Additionally, the application of STMR in conjunction with real-time control and perception modules could further facilitate the use of robots in complex, real-world scenarios.

Overall, this paper provides a rigorous and thoughtful contribution to the motion imitation field, emphasizing the nuanced challenges and solutions within the context of transferring detailed animal motions to robotic counterparts.

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