no code implementations • 1 Feb 2022 • Haresh Karnan, Garrett Warnell, Faraz Torabi, Peter Stone
The imitation learning research community has recently made significant progress towards the goal of enabling artificial agents to imitate behaviors from video demonstrations alone.
no code implementations • 13 Jul 2021 • Ruohan Zhang, Faraz Torabi, Garrett Warnell, Peter Stone
A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making.
no code implementations • 15 Apr 2021 • Eddy Hudson, Garrett Warnell, Faraz Torabi, Peter Stone
Learning from demonstrations in the wild (e. g. YouTube videos) is a tantalizing goal in imitation learning.
no code implementations • 31 Mar 2021 • Faraz Torabi, Garrett Warnell, Peter Stone
In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator.
no code implementations • 21 Sep 2019 • Ruohan Zhang, Faraz Torabi, Lin Guan, Dana H. Ballard, Peter Stone
Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment.
no code implementations • 18 Jun 2019 • Brahma S. Pavse, Faraz Torabi, Josiah P. Hanna, Garrett Warnell, Peter Stone
Augmenting reinforcement learning with imitation learning is often hailed as a method by which to improve upon learning from scratch.
no code implementations • 18 Jun 2019 • Faraz Torabi, Sean Geiger, Garrett Warnell, Peter Stone
We test our algorithm and conduct experiments using an imitation task on a physical robot arm and its simulated version in Gazebo and will show the improvement in learning rate and efficiency.
no code implementations • 30 May 2019 • Faraz Torabi, Garrett Warnell, Peter Stone
Imitation learning is the process by which one agent tries to learn how to perform a certain task using information generated by another, often more-expert agent performing that same task.
no code implementations • 22 May 2019 • Faraz Torabi, Garrett Warnell, Peter Stone
Classically, imitation learning algorithms have been developed for idealized situations, e. g., the demonstrations are often required to be collected in the exact same environment and usually include the demonstrator's actions.
1 code implementation • 17 Jul 2018 • Faraz Torabi, Garrett Warnell, Peter Stone
Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions.
5 code implementations • 4 May 2018 • Faraz Torabi, Garrett Warnell, Peter Stone
In this work, we propose a two-phase, autonomous imitation learning technique called behavioral cloning from observation (BCO), that aims to provide improved performance with respect to both of these aspects.