# Spatio-Temporal Motion Retargeting for Quadruped Robots

arXiv: https://arxiv.org/abs/2404.11557 · HTML: https://mlsanity.com/paper/2404.11557/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2024.

## Built on by (most cited first, 8 of 8)

- [Multi-Embodiment Robotic Retargeting via Guided Diffusion Model](https://mlsanity.com/paper/2505.20857/) (2025)
- [MoReFlow: Motion Retargeting Learning through Unsupervised Flow Matching](https://mlsanity.com/paper/2509.25600/) (2025)
- [Flip Stunts on Bicycle Robots using Iterative Motion Imitation](https://mlsanity.com/paper/2603.27944/) (2026)
- [Direct Dynamic Retargeting for Humanoid Imitation Learning from Videos](https://mlsanity.com/paper/2605.23762/) (2026)
- [MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation](https://mlsanity.com/paper/2407.17502/) (2024)
- [PUMA: Perception-driven Unified Foothold Prior for Mobility Augmented Quadruped Parkour](https://mlsanity.com/paper/2601.15995/) (2026)
- [OmniMimic: Dynamics-completed Motion Augmentation for Multi-style Omnidirectional Quadruped Locomotion](https://mlsanity.com/paper/2609.20566/) (2026)
- [CopyQuad: Quadrupedal Skill Transfer from a Single Side-View Video Without a Depth Model](https://mlsanity.com/paper/2610.16737/) (2026)

## Abstract (arXiv)

This work presents a motion retargeting approach for legged robots, aimed at transferring the dynamic and agile movements to robots from source motions. In particular, we guide the imitation learning procedures by transferring motions from source to target, effectively bridging the morphological disparities while ensuring the physical feasibility of the target system. In the first stage, we focus on motion retargeting at the kinematic level by generating kinematically feasible whole-body motions from keypoint trajectories. Following this, we refine the motion at the dynamic level by adjusting it in the temporal domain while adhering to physical constraints. This process facilitates policy training via reinforcement learning, enabling precise and robust motion tracking. We demonstrate that our approach successfully transforms noisy motion sources, such as hand-held camera videos, into robot-specific motions that align with the morphology and physical properties of the target robots. Moreover, we demonstrate terrain-aware motion retargeting to perform BackFlip on top of a box. We successfully deployed these skills to four robots with different dimensions and physical properties in the real world through hardware experiments.