Publications (5)
CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving
Jonathan Booher, Khashayar Rohanimanesh, Junhong Xu +5
Modern approaches to autonomous driving rely heavily on learned components trained with large amounts of human driving data via imitation learning. However, these methods require l…
Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity
Ajay Mandlekar, Jonathan Booher, Max Spero +6
Large, richly annotated datasets have accelerated progress in fields such as computer vision and natural language processing, but replicating these successes in robotics has been c…
RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation
Ajay Mandlekar, Yuke Zhu, Animesh Garg +9
Imitation Learning has empowered recent advances in learning robotic manipulation tasks by addressing shortcomings of Reinforcement Learning such as exploration and reward specific…
Uniformly Safe RL with Objective Suppression for Multi-Constraint Safety-Critical Applications
Zihan Zhou, Jonathan Booher, Khashayar Rohanimanesh +3
Safe reinforcement learning tasks are a challenging domain despite being very common in the real world. The widely adopted CMDP model constrains the risks in expectation, which mak…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…