3 citations · 6 across the 6 of their papers we have counts for
6 papers
DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos
Shenyuan Gao, William Liang, Kaiyuan Zheng +27
Being able to simulate the outcomes of actions in varied environments will revolutionize the development of generalist agents at scale. However, modeling these world dynamics, espe…
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…
Generating Behaviorally Diverse Policies with Latent Diffusion Models
Shashank Hegde, Sumeet Batra, K. R. Zentner +1
Recent progress in Quality Diversity Reinforcement Learning (QD-RL) has enabled learning a collection of behaviorally diverse, high performing policies. However, these methods typi…
A Simple Approach to Continual Learning by Transferring Skill Parameters
K. R. Zentner, Ryan Julian, Ujjwal Puri +2
In order to be effective general purpose machines in real world environments, robots not only will need to adapt their existing manipulation skills to new circumstances, they will…
Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning
K. R. Zentner, Ryan Julian, Ujjwal Puri +2
We explore possible methods for multi-task transfer learning which seek to exploit the shared physical structure of robotics tasks. Specifically, we train policies for a base set o…