15 citations · 90 across the 20 of their papers we have counts for
12 papers · 1 filter
Improving Pre-Trained Vision-Language-Action Policies with Model-Based Search
Cyrus Neary, Omar G. Younis, Artur Kuramshin +2
Pre-trained vision-language-action (VLA) models offer a promising foundation for generalist robot policies, but often produce brittle behaviors or unsafe failures when deployed zer…
RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies
Pranav Atreya, Karl Pertsch, Tony Lee +29
Comprehensive, unbiased, and comparable evaluation of modern generalist policies is uniquely challenging: existing approaches for robot benchmarking typically rely on heavy standar…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair +98
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. Ho…
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control
Zhongyu Li, Xue Bin Peng, Pieter Abbeel +3
This paper presents a comprehensive study on using deep reinforcement learning (RL) to create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single l…
Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning
Jensen Gao, Siddharth Reddy, Glen Berseth +2
Adaptive interfaces can help users perform sequential decision-making tasks like robotic teleoperation given noisy, high-dimensional command signals (e.g., from a brain-computer in…
Robust and Versatile Bipedal Jumping Control through Reinforcement Learning
Zhongyu Li, Xue Bin Peng, Pieter Abbeel +3
This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled bipedal robot to perform robust and versatile dynamic jumps in the real world. We pr…
Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot
Yandong Ji, Zhongyu Li, Yinan Sun +4
We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning. Developing algorithms to enable a legged ro…
ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning
Sean Chen, Jensen Gao, Siddharth Reddy +3
Building assistive interfaces for controlling robots through arbitrary, high-dimensional, noisy inputs (e.g., webcam images of eye gaze) can be challenging, especially when it invo…
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots
Zhongyu Li, Xuxin Cheng, Xue Bin Peng +4
Developing robust walking controllers for bipedal robots is a challenging endeavor. Traditional model-based locomotion controllers require simplifying assumptions and careful model…
Morphology-Agnostic Visual Robotic Control
Brian Yang, Dinesh Jayaraman, Glen Berseth +2
Existing approaches for visuomotor robotic control typically require characterizing the robot in advance by calibrating the camera or performing system identification. We propose M…
Contextual Imagined Goals for Self-Supervised Robotic Learning
Ashvin Nair, Shikhar Bahl, Alexander Khazatsky +3
While reinforcement learning provides an appealing formalism for learning individual skills, a general-purpose robotic system must be able to master an extensive repertoire of beha…