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...
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...
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...
Diffuse-CLoC: Guided Diffusion for Physics-based Character Look-ahead Control
Researchers present Diffuse-CLoC, a guided diffusion framework for physics-based look-ahead control that enables intuitive, steerable, and physically realistic motion generation.
Accelerating Residual Policy Learning with Uncertainty Estimation
We propose two improvements to Residual RL that further enhance its sample efficiency and make it suitable for stochastic base policies.
Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control
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...
High-Performance Reinforcement Learning on Spot: Optimizing Simulation Parameters with Distributional Measures
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...
Force Admittance Control of an Underactuated Gripper with Full-State Feedback
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...
Should We Learn Contact-Rich Manipulation Policies from Sampling-Based Planners?
Researchers investigate how to leverage model-based planning and optimization to generate training data for contact-rich dexterous manipulation tasks.