Yao Mu

Training-time Gaussian distillation can elevate WAM performance by over 19% by effectively integrating geometric and semantic information without altering deployment architecture.

Fine-grained metrics reveal that robots can recover from failures more effectively than previously thought, reshaping our understanding of their capabilities.

Enfold redefines how we leverage world generative models, enabling ultra-efficient control that adapts dynamically to real-time changes in the environment.

The data pyramid framework reveals how the interplay of diverse data sources can unlock new capabilities in embodied agents, highlighting critical gaps in current methodologies.

Achieving a 78.3% success rate in real-world mobile manipulation, this framework bridges the reality gap with zero-shot transferability to unseen tasks.

DenseReward synthesizes diverse failure trajectories automatically, enabling robots to learn from a rich array of failure modes without human labeling.

By preserving the semantics of pretrained models while achieving superior compositional generalization, InternVLA-A1.5 redefines how robots can learn and execute complex tasks.

RoboDojo reveals that integrating simulation and real-world tasks can significantly enhance the evaluation of robot manipulation policies, bridging the gap between theoretical performance and practical deployment.

Orca's unified world latent space enables superior performance in diverse tasks, outperforming specialized models with a single framework.