12 citations · 21 across the 7 of their papers we have counts for
14 papers
ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning
Jayjun Lee, Jessica Yin, Asif Rana +7
We introduce Accelerating Dexterity via Pre-Training (ADEPT), a large-scale reinforcement learning (RL) framework for learning sim-to-real transferable dexterity across high degree…
ZEST: Zero-shot Embodied Skill Transfer for Athletic Robot Control
Jean Pierre Sleiman, He Li, Alphonsus Adu-Bredu +25
Achieving robust, human-like whole-body control on humanoid robots for agile, contact-rich behaviors remains a central challenge, demanding heavy per-skill engineering and a brittl…
Dynamic Non-Prehensile Object Transport via Model-Predictive Reinforcement Learning
Neel Jawale, Byron Boots, Balakumar Sundaralingam +1
We investigate the problem of teaching a robot manipulator to perform dynamic non-prehensile object transport, also known as the `robot waiter' task, from a limited set of real-wor…
Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning
Mohak Bhardwaj, Thomas Lampe, Michael Neunert +6
Recent advances in real-world applications of reinforcement learning (RL) have relied on the ability to accurately simulate systems at scale. However, domains such as fluid dynamic…
Adversarial Model for Offline Reinforcement Learning
Mohak Bhardwaj, Tengyang Xie, Byron Boots +2
We propose a novel model-based offline Reinforcement Learning (RL) framework, called Adversarial Model for Offline Reinforcement Learning (ARMOR), which can robustly learn policies…
ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data
Tengyang Xie, Mohak Bhardwaj, Nan Jiang +1
We propose a new model-based offline RL framework, called Adversarial Models for Offline Reinforcement Learning (ARMOR), which can robustly learn policies to improve upon an arbitr…