Pieter Abbeel

Abstract

We introduce Contrastive Intrinsic Control (CIC) - an algorithm for unsupervised skill discovery that maximizes the mutual information between skills and state transitions. In contrast to most prior approaches, CIC uses a decomposition of the mutual information that explicitly incentivizes diverse behaviors by...

Abstract

We introduce Masked Trajectory Models (MTM) as a generic abstraction for sequential decision making. MTM takes a trajectory, such as a state-action sequence, and aims to reconstruct the trajectory conditioned on random subsets of the same trajectory. By training with a highly randomized masking pattern, MTM learns versatile networks that can take on different roles or capabilities, by simply choosing appropriate masks at inference time. For example, the same MTM network can be used as a forward dynamics model, inverse dynamics model, or even an...

In this project, we look to develop methods that learn world models for agents to solve difficult real-world robotics tasks. Specifically, we focus on various real-world tasks, such as cable manipulation, that require very fine-grained details of a scene to accurately model future predictions. To do this, we will explore models that localize spatial regions of interest in images, and construct patch or region-based selection methods to acquire fine-grained detailed information.