We examine the motivations behind building documentation tools, how authors conceptualize documentation practices, and how these tools connect to existing systems, regulations, and cultural norms.
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Dull, Dirty, Dangerous: Understanding the Past, Present, and Future of a Key Motivation for Robotics
In robotics, the concept of “dull, dirty, and dangerous” (DDD) work has been used to motivate where robots might be useful. In this paper, we...
RAI Institute 2025: A Year of Innovation for Robotics and AI
There’s a world where robots integrate seamlessly into our daily work, and eventually our cities and home lives. When the RAI Institute was founded in...
ROVER: Recursive Reasoning Over Videos with Vision-Language Models for Embodied Tasks
We propose ROVER (Reasoning Over VidEo Recursively), a framework that enables the model to recursively decompose long horizon video trajectories into segments corresponding to shorter...
A Dynamic Robot That Can Throw, Catch, and Hit a Baseball
Researchers at the RAI Institute have built a low-impedance platform to study dynamic robot manipulation. In this demo, robots play a game of catch and...
From Walking to Working: Spot Stacks Tires
Spot robot performs dynamic whole-body manipulation using a combination of reinforcement learning and sampling-based control. Behavior shown in the video is fully autonomous, including the...
Combining Sampling and Learning for Dynamic Whole-Body Manipulation
Spot uses dynamic whole-body manipulation to autonomously upright, roll, drag, and stack 15kg car tires using an approach that combines reinforcement learning and sampling-based optimization
ParticleFormer: A 3D Point Cloud World Model for Multi-Object, Multi-Material Robotic Manipulation
We present ParticleFormer, a Transformer-based point cloud world model trained with a hybrid point cloud reconstruction loss, supervising both global and local dynamics features in...
Learning Equivariant Neural-Augmented Object Dynamics from Few Interactions
We introduce PIEGraph, a neural-augmented dynamics model capable of learning physically-grounded object dynamics for rigid and deformable bodies from few interactions.
ReLIC: Versatile Loco-Manipulation through Flexible Interlimb Coordination
Abstract: The ability to flexibly leverage limbs for loco-manipulation is essential for enabling autonomous robots to operate in unstructured environments. Yet, prior work on loco-manipulation...
The ReLIC Framework: Advancing Robotics with Flexible Loco-Manipulation
New reinforcement learning approach enables robots to dynamically coordinate any combination of limbs for complex tasks
Ultra Mobile Vehicle Expands Mobility Repertoire
Using reinforcement learning we have expanded the range of techniques the Ultra Mobile Vehicle (UMV) uses to handle terrain and obstacles, including hops, out-of-plane balance,...