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Researchers at the RAI Institute have bridged the gap between kinematic motion diffusion models and physics-based control policies with a new guided diffusion framework called...
This workshop aims to gather experts in 3D human/animal modeling, motion generation algorithms, and robotics to review recent progress and chart the next steps for...
Researchers present Diffuse-CLoC, a guided diffusion framework for physics-based look-ahead control that enables intuitive, steerable, and physically realistic motion generation.
We introduce Judo, a software package designed to address this need. To facilitate rapid prototyping and evaluation, Judo provides robust implementations of common sampling-based MPC...
The IEEE International Conference on Robotics and Automation (ICRA) will take place May 19 – 23, 2025 in Atlanta, Georgia. ICRA serves as the premier...
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...
To move closer to the goal of next-generation hardware design capable of more human-like manipulation, researchers at the Institute have developed a new type of...
Researchers investigate how to leverage model-based planning and optimization to generate training data for contact-rich dexterous manipulation tasks.
Ultra Mobile Vehicle (UMV) learns to flip.
ASHiTA alternates LLM-assisted hierarchical task analysis, to generate the task breakdown, with task-driven 3D scene graph construction to generate a suitable representation of the environment.
Reinforcement learning is used to speed the production of behavior for the Atlas humanoid robot. At the heart of the learning process is a physics-based...
Researchers at the Institute examine the effectiveness of linear feedback controllers for contact-rich robotic manipulation, with differentiable simulations that use contact smoothing.
