To support foundation-model learning on humanoids and quadruped robots, we develop whole-body teleoperation interfaces and data-collection environments. From affordable …
We extend vision-language-action (VLA) foundation models with attention-guided mask images, making the correspondence between natural-language and visual instructions explicit so …
We apply foundation models such as GPT to humanoid motor control, building unified policies that handle diverse motion tasks including locomotion and whole-body actions. By …
We study world-model-based reinforcement learning for mobile manipulation that jointly addresses embodiment selection (when to move the base vs. use the arm) and motion planning. …
Using large language models (LLMs) and vision-language models (VLMs), the robot autonomously generates demonstrations from task instructions, enabling motion-skill learning without …