1 code implementation • 9 Jan 2017 • Tanmay Shankar, Santosha K. Dwivedy, Prithwijit Guha
Deep Reinforcement Learning has enabled the learning of policies for complex tasks in partially observable environments, without explicitly learning the underlying model of the tasks.
no code implementations • 20 Jun 2018 • Tanmay Shankar, Nicholas Rhinehart, Katharina Muelling, Kris M. Kitani
We introduce a novel deterministic policy gradient update, DRAG (i. e., DeteRministically AGgrevate) in the form of a deterministic actor-critic variant of AggreVaTeD, to train our neural parser.
no code implementations • ICLR 2020 • Tanmay Shankar, Shubham Tulsiani, Lerrel Pinto, Abhinav Gupta
In this paper, we present an approach to learn recomposable motor primitives across large-scale and diverse manipulation demonstrations.
no code implementations • ICML 2020 • Tanmay Shankar, Abhinav Gupta
In this paper, we address the discovery of robotic options from demonstrations in an unsupervised manner.
no code implementations • 29 Sep 2021 • Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh
In this paper, we explore how we can endow robots with the ability to learn correspondences between their own skills, and those of morphologically different robots in different domains, in an entirely unsupervised manner.