Conference

We present VMS, a unified whole-body controller that enables humanoid robots to learn diverse and dynamic behaviors within a single policy through hybrid tracking and orthogonal mixture of experts.

We propose Align-Then-stEer (ATE), a framework that adapts VLAs to novel robots and tasks through unified latent guidance. ATE can handle significant domain shifts without compromising performance and compatible to Pi0, RDT, and etc.

We introduce X-Loco, a framework for training a vision-based generalist humanoid locomotion controller through synergetic policy distillation.

We address humanoid skateboarding, a highly challenging task requiring stable dynamic maneuvering on an humanoid platform via physics-aware whole-body control for humanoid skateboarding in dynamic settings.

V2A unifies dynamics alignment, value alignment, and value assignment for heterogeneous cross-domain offline RL and improves robust source-data filtering.

Re^2MoGen combines LLM reasoning, keyframe-guided completion, and physics-aware RL refinement for open-vocabulary text-to-motion generation.

We present foundation VLN model via developing an automated pipeline for large-scale navigation data generation, resulting in the GN-Matrix dataset and a high-fidelity simulation platform that supports interactive roaming and collision-aware navigation.

We introduce PRTS, a New Generation of Reinforcement Learning-Native Robotic Vision-Language-Action(VLA) foundation model.