90 citations · 113 across the 8 of their papers we have counts for
11 papers
Adversarial Imitation Learning from Video using a State Observer
Haresh Karnan, Garrett Warnell, Faraz Torabi +1
The imitation learning research community has recently made significant progress towards the goal of enabling artificial agents to imitate behaviors from video demonstrations alone…
Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks
Ruohan Zhang, Faraz Torabi, Garrett Warnell +1
A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making. Importantly, while it is…
Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch
Eddy Hudson, Garrett Warnell, Faraz Torabi +1
Learning from demonstrations in the wild (e.g. YouTube videos) is a tantalizing goal in imitation learning. However, for this goal to be achieved, imitation learning algorithms mus…
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation
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 contr…
Leveraging Human Guidance for Deep Reinforcement Learning Tasks
Ruohan Zhang, Faraz Torabi, Lin Guan +2
Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment. Human knowledge of how to solve these tasks can be incorporated usin…
Recent Advances in Imitation Learning from Observation
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 sa…
Imitation Learning from Video by Leveraging Proprioception
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 a…
Sample-efficient Adversarial Imitation Learning from Observation
Faraz Torabi, Sean Geiger, Garrett Warnell +1
Imitation from observation is the framework of learning tasks by observing demonstrated state-only trajectories. Recently, adversarial approaches have achieved significant performa…
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration
Brahma S. Pavse, Faraz Torabi, Josiah P. Hanna +2
Augmenting reinforcement learning with imitation learning is often hailed as a method by which to improve upon learning from scratch. However, most existing methods for integrating…
Generative Adversarial Imitation from Observation
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. The lack of action informat…
Behavioral Cloning from Observation
Faraz Torabi, Garrett Warnell, Peter Stone
Humans often learn how to perform tasks via imitation: they observe others perform a task, and then very quickly infer the appropriate actions to take based on their observations.…