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Found 11 papers, 2 papers with code
Adversarial Imitation Learning from Video using a State Observer
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
A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making.
Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch
Learning from demonstrations in the wild (e. g. YouTube videos) is a tantalizing goal in imitation learning.
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation
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.
Leveraging Human Guidance for Deep Reinforcement Learning Tasks
Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment.
Sample-efficient Adversarial Imitation Learning from Observation
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.
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration
Augmenting reinforcement learning with imitation learning is often hailed as a method by which to improve upon learning from scratch.
Recent Advances in Imitation Learning from Observation
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.
Imitation Learning from Video by Leveraging Proprioception
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.
Generative Adversarial Imitation from Observation
Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions.
Behavioral Cloning from Observation
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.
