Fangzhou Yu

Pranay Dugar, Aayam Shrestha, Fangzhou Yu et al.

The foundational capabilities of humanoid robots should include robustly standing, walking, and mimicry of whole and partial-body motions. This work introduces the Masked Humanoid Controller (MHC), which supports all of these capabilities by tracking target trajectories over selected subsets of humanoid state variables while ensuring balance and robustness against disturbances. The MHC is trained in simulation using a carefully designed curriculum that imitates partially masked motions from a library of behaviors spanning standing, walking, optimized reference trajectories, re-targeted video clips, and human motion capture data. It also allows for combining joystick-based control with partial-body motion mimicry. We showcase simulation experiments validating the MHC's ability to execute a wide variety of behaviors from partially-specified target motions. Moreover, we demonstrate sim-to-real transfer on the real-world Digit V3 humanoid robot. To our knowledge, this is the first instance of a learned controller that can realize whole-body control of a real-world humanoid for such diverse multi-modal targets.

AJ Miller, Fangzhou Yu, Michael Brauckmann et al.

This work presents an overview of the technical details behind a high performance reinforcement learning policy deployment with the Spot RL Researcher Development Kit for low level motor access on Boston Dynamics Spot. This represents the first public demonstration of an end to end end reinforcement learning policy deployed on Spot hardware with training code publicly available through Nvidia IsaacLab and deployment code available through Boston Dynamics. We utilize Wasserstein Distance and Maximum Mean Discrepancy to quantify the distributional dissimilarity of data collected on hardware and in simulation to measure our sim2real gap. We use these measures as a scoring function for the Covariance Matrix Adaptation Evolution Strategy to optimize simulated parameters that are unknown or difficult to measure from Spot. Our procedure for modeling and training produces high quality reinforcement learning policies capable of multiple gaits, including a flight phase. We deploy policies capable of over 5.2ms locomotion, more than triple Spots default controller maximum speed, robustness to slippery surfaces, disturbance rejection, and overall agility previously unseen on Spot. We detail our method and release our code to support future work on Spot with the low level API.

Xiaoyu Huang, Takara Truong, Yunbo Zhang et al.

We present Diffuse-CLoC, a guided diffusion framework for physics-based look-ahead control that enables intuitive, steerable, and physically realistic motion generation. While existing kinematics motion generation with diffusion models offer intuitive steering capabilities with inference-time conditioning, they often fail to produce physically viable motions. In contrast, recent diffusion-based control policies have shown promise in generating physically realizable motion sequences, but the lack of kinematics prediction limits their steerability. Diffuse-CLoC addresses these challenges through a key insight: modeling the joint distribution of states and actions within a single diffusion model makes action generation steerable by conditioning it on the predicted states. This approach allows us to leverage established conditioning techniques from kinematic motion generation while producing physically realistic motions. As a result, we achieve planning capabilities without the need for a high-level planner. Our method handles a diverse set of unseen long-horizon downstream tasks through a single pre-trained model, including static and dynamic obstacle avoidance, motion in-betweening, and task-space control. Experimental results show that our method significantly outperforms the traditional hierarchical framework of high-level motion diffusion and low-level tracking.