Search Results for author:
Found 5 papers, 2 papers with code
AutoEval: Autonomous Evaluation of Generalist Robot Manipulation Policies in the Real World
Scalable and reproducible policy evaluation has been a long-standing challenge in robot learning.
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
A robot foundation model, trained on massive and diverse data sources, is essential for enabling the robots to reason about novel situations, robustly handle real-world variability, and rapidly learn new tasks.
Octo: An Open-Source Generalist Robot Policy
In experiments across 9 robotic platforms, we demonstrate that Octo serves as a versatile policy initialization that can be effectively finetuned to new observation and action spaces.
Ranked #3 on
Robot Manipulation
on SimplerEnv-Widow X
(using extra training data)
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning
We posit that a significant challenge to widespread adoption of robotic RL, as well as further development of robotic RL methods, is the comparative inaccessibility of such methods.
ForceSight: Text-Guided Mobile Manipulation with Visual-Force Goals
Given a single RGBD image combined with a text prompt, ForceSight determines a target end-effector pose in the camera frame (kinematic goal) and the associated forces (force goal).
