We find that across a wide range of robot policy learning scenarios, treating supervised
policy learning with an implicit model generally performs better, on average, than commonly …

We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors
designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 …

Prompt-based learning has emerged as a successful paradigm in natural language
processing, where a single general-purpose language model can be instructed to perform …

Despite the success of deep neural networks, there are still many challenges in deep
representation learning due to the data scarcity issues such as data imbalance, unseen …

Scaling end-to-end reinforcement learning to control real robots from vision presents a
series of challenges, in particular in terms of sample efficiency. Against end-to-end learning …

Prediction is an appealing objective for self-supervised learning of behavioral skills,
particularly for autonomous robots. However, effectively utilizing predictive models for …

The grand aim of having a single robot that can manipulate arbitrary objects in diverse
settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets …

Well structured visual representations can make robot learning faster and can improve
generalization. In this paper, we study how we can acquire effective object-centric …

Improving the generalization capabilities of general-purpose robotic agents has long been a
significant challenge actively pursued by research communities. Existing approaches often …

Abstract Research on Inverse Reinforcement Learning (IRL) from third-person videos has
shown encouraging results on removing the need for manual reward design for robotic …