M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking

• URL: http://arxiv.org/abs/2606.04829v1
• Date: Wed, 03 Jun 2026 12:52:37 GMT
• Title: M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking
• Abstract summary: Multi-Modal Mimic is a versatile whole-body control framework for humanoid robots.<n>It unifies heterogeneous motion reference modalities, including robot joint angles, human pose trajectories, and end-effector poses.<n>It achieves sim-to-real transfer across multiple motion reference modalities without modality-specific retraining.
• Score: 45.636871702422134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building a general-purpose whole-body controller is essential for enabling diverse motion capabilities in humanoid robots across a wide range of downstream tasks, including locomotion and loco-manipulation. Different tasks rely on distinct motion reference modalities: locomotion primarily depends on coordinated robot joint trajectories, whereas manipulation requires precise end-effector trajectory tracking. Existing methods often overlook the representational mismatch between dense robot joint angles and sparse end-effector poses. To address this, we propose Multi-Modal Mimic (M3imic), a versatile multi-modal whole-body control framework that unifies heterogeneous motion reference modalities, including robot joint angles, human pose trajectories, and end-effector poses, using modality-specific encoders to map them into a shared latent space. Leveraging large-scale reinforcement learning in the simulator, we train a single policy that achieves sim-to-real transfer across multiple motion reference modalities without modality-specific retraining. Extensive simulation and real-world experiments on the Unitree G1 robot are conducted to evaluate the proposed framework. In simulation, the policy achieves a peak success rate of 98.42\% on an unseen test dataset, demonstrating its exceptional generalization capability. The code is available at https://github.com/Renforce-Dynamics/MultiModalWBC

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