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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 726–750 of 2122 papers

TitleStatusHype
Diffusion Model-Augmented Behavioral Cloning—0
BlockPuzzle - A Challenge in Physical Reasoning and Generalization for Robot Learning—0
An Energy-Aware Online Learning Framework for Resource Management in Heterogeneous Platforms—0
Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning—0
Diffusion Imitation from Observation—0
Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning—0
Diffusion-Based Imitation Learning for Social Pose Generation—0
Blending Imitation and Reinforcement Learning for Robust Policy Improvement—0
Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
Differentiable Robust LQR Layers—0
Bi-LAT: Bilateral Control-Based Imitation Learning via Natural Language and Action Chunking with Transformers—0
Differentiable Constrained Imitation Learning for Robot Motion Planning and Control—0
DIDA: Denoised Imitation Learning based on Domain Adaptation—0
An Analysis of Logit Learning with the r-Lambert Function—0
Adaptive t-Momentum-based Optimization for Unknown Ratio of Outliers in Amateur Data in Imitation Learning—0
A Comparison of Imitation Learning Algorithms for Bimanual Manipulation—0
DIAL: Distribution-Informed Adaptive Learning of Multi-Task Constraints for Safety-Critical Systems—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
Dexterous Manipulation through Imitation Learning: A Survey—0
Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation—0
DexterityGen: Foundation Controller for Unprecedented Dexterity—0
Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration—0
An Algorithmic Perspective on Imitation Learning—0
Adaptive Synthetic Characters for Military Training—0
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