Implicit Kinodynamic Motion Retargeting for Human-to-humanoid Imitation Learning

Human-to-humanoid imitation learning aims to learn a humanoid whole-body controller from human motion. Motion retargeting is a crucial step in enabling robots to acquire reference trajectories when exploring locomotion skills. However, current methods focus on motion retargeting frame by frame, which lacks scalability. Could we directly convert large-scale human motion into robot-executable motion through a more efficient approach? To address this issue, we propose Implicit Kinodynamic Motion Retargeting (IKMR), a novel efficient and scalable retargeting framework that considers both kinematics and dynamics. In kinematics, IKMR pretrains motion topology feature representation and a dual encoder-decoder architecture to learn a motion domain mapping. In dynamics, IKMR integrates imitation learning with the motion retargeting network to refine motion into physically feasible trajectories. After fine-tuning using the tracking results, IKMR can achieve large-scale physically feasible motion retargeting in real time, and a whole-body controller could be directly trained and deployed for tracking its retargeted trajectories. We conduct our experiments both in the simulator and the real robot on a full-size humanoid robot. Extensive experiments and evaluation results verify the effectiveness of our proposed framework.

State Estimation Transformers for Agile Legged Locomotion

We propose a state estimation method that can accurately predict the robot's privileged states to push the limits of quadruped robots in executing advanced skills such as jumping in the wild. In particular, we present the State Estimation Transformers (SET), an architecture that casts the state estimation problem as conditional sequence modeling. SET outputs the robot states that are hard to obtain directly in the real world, such as the body height and velocities, by leveraging a causally masked Transformer. By conditioning an autoregressive model on the robot's past states, our SET model can predict these privileged observations accurately even in highly dynamic locomotions. We evaluate our methods on three tasks -- running jumping, running backflipping, and running sideslipping -- on a low-cost quadruped robot, Cyberdog2. Results show that SET can outperform other methods in estimation accuracy and transferability in the simulation as well as success rates of jumping and triggering a recovery controller in the real world, suggesting the superiority of such a Transformer-based explicit state estimator in highly dynamic locomotion tasks.