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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 751–800 of 2122 papers

TitleStatusHype
Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera—0
Automating Deformable Gasket Assembly—0
Cross Domain Imitation Learning—0
Goal-conditioned Imitation Learning—0
Exploring the use of deep learning in task-flexible ILC—0
Exploring the trade off between human driving imitation and safety for traffic simulation—0
Goal-Directed Design Agents: Integrating Visual Imitation with One-Step Lookahead Optimization for Generative Design—0
Goal-Driven Imitation Learning from Observation by Inferring Goal Proximity—0
Co-Imitation: Learning Design and Behaviour by Imitation—0
AGIL: Learning Attention from Human for Visuomotor Tasks—0
Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective—0
Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations—0
A Strong Baseline for Batch Imitation Learning—0
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots—0
A Statistical Guarantee for Representation Transfer in Multitask Imitation Learning—0
Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues—0
Graph Neural Networks for Decentralized Multi-Agent Perimeter Defense—0
Graph Neural Networks for Multi-Robot Active Information Acquisition—0
Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation—0
Action-Free Reasoning for Policy Generalization—0
Language Conditioned Imitation Learning over Unstructured Data—0
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations—0
GRP Model for Sensorimotor Learning—0
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
Exploration Based Language Learning for Text-Based Games—0
Guided Meta-Policy Search—0
Explaining Imitation Learning through Frames—0
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents—0
CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity—0
CMR-Agent: Learning a Cross-Modal Agent for Iterative Image-to-Point Cloud Registration—0
Curriculum Offline Imitating Learning—0
GymFG: A Framework with a Gym Interface for FlightGear—0
Hyperparameter Selection for Imitation Learning—0
Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale—0
Explaining Fast Improvement in Online Imitation Learning—0
Explaining Autonomous Driving by Learning End-to-End Visual Attention—0
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories—0
Signs of Language: Embodied Sign Language Fingerspelling Acquisition from Demonstrations for Human-Robot Interaction—0
HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation—0
HE-Drive: Human-Like End-to-End Driving with Vision Language Models—0
Explainable Hierarchical Imitation Learning for Robotic Drink Pouring—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration—0
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation—0
Coarse-to-Fine 3D Keyframe Transporter—0
Imitation Learning from Pixel-Level Demonstrations by HashReward—0
Hierarchical Imitation Learning for Stochastic Environments—0
Hierarchical Imitation Learning of Team Behavior from Heterogeneous Demonstrations—0
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback—0
Adversarial Imitation Learning via Random Search—0
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