226 citations · 1k across the 102 of their papers we have counts for
26 papers · 1 filter
Guaranteed Trust Region Optimization via Two-Phase KL Penalization
K. R. Zentner, Ujjwal Puri, Zhehui Huang +1
On-policy reinforcement learning (RL) has become a popular framework for solving sequential decision problems due to its computational efficiency and theoretical simplicity. Some o…
Conditionally Combining Robot Skills using Large Language Models
K. R. Zentner, Ryan Julian, Brian Ichter +1
This paper combines two contributions. First, we introduce an extension of the Meta-World benchmark, which we call "Language-World," which allows a large language model to operate…
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…
HyperPPO: A scalable method for finding small policies for robotic control
Shashank Hegde, Zhehui Huang, Gaurav S. Sukhatme
Models with fewer parameters are necessary for the neural control of memory-limited, performant robots. Finding these smaller neural network architectures can be time-consuming. We…
Exploiting Generalization in Offline Reinforcement Learning via Unseen State Augmentations
Nirbhay Modhe, Qiaozi Gao, Ashwin Kalyan +3
Offline reinforcement learning (RL) methods strike a balance between exploration and exploitation by conservative value estimation -- penalizing values of unseen states and actions…
LEMMA: Learning Language-Conditioned Multi-Robot Manipulation
Ran Gong, Xiaofeng Gao, Qiaozi Gao +3
Complex manipulation tasks often require robots with complementary capabilities to collaborate. We introduce a benchmark for LanguagE-Conditioned Multi-robot MAnipulation (LEMMA) f…