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Imitation Learning

Imitation Learning is a framework for learning a behavior policy from demonstrations. Usually, demonstrations are presented in the form of state-action trajectories, with each pair indicating the action to take at the state being visited. In order to learn the behavior policy, the demonstrated actions are usually utilized in two ways. The first, known as Behavior Cloning (BC), treats the action as the target label for each state, and then learns a generalized mapping from states to actions in a supervised manner. Another way, known as Inverse Reinforcement Learning (IRL), views the demonstrated actions as a sequence of decisions, and aims at finding a reward/cost function under which the demonstrated decisions are optimal.

Finally, a newer methodology, Inverse Q-Learning aims at directly learning Q-functions from expert data, implicitly representing rewards, under which the optimal policy can be given as a Boltzmann distribution similar to soft Q-learning

Source: Learning to Imitate

Papers

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Showing 1026–1050 of 2122 papers

TitleStatusHype
ChicGrasp: Imitation-Learning based Customized Dual-Jaw Gripper Control for Delicate, Irregular Bio-products Manipulation—0
Enhancing Reusability of Learned Skills for Robot Manipulation via Gaze and Bottleneck—0
Learning dissection trajectories from expert surgical videos via imitation learning with equivariant diffusion—0
Challenging Common Assumptions in Convex Reinforcement Learning—0
Enhancing Autonomous Driving Safety with Collision Scenario Integration—0
ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning—0
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards—0
Enhanced DACER Algorithm with High Diffusion Efficiency—0
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning—0
Learning Diverse Robot Striking Motions with Diffusion Models and Kinematically Constrained Gradient Guidance—0
Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving—0
EnerVerse-AC: Envisioning Embodied Environments with Action Condition—0
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning—0
Chain of Thought Imitation with Procedure Cloning—0
Ark: An Open-source Python-based Framework for Robot Learning—0
End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning—0
End-to-End Stable Imitation Learning via Autonomous Neural Dynamic Policies—0
CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning—0
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory—0
Deep Visual Navigation under Partial Observability—0
Car-Following Models: A Multidisciplinary Review—0
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces—0
Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models—0
End-to-end Manipulator Calligraphy Planning via Variational Imitation Learning—0
Decentralized Multi-Agents by Imitation of a Centralized Controller—0
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