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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 651–700 of 2122 papers

TitleStatusHype
ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback—0
UNIQ: Offline Inverse Q-learning for Avoiding Undesirable DemonstrationsCode0
Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman ControllersCode0
Mastering Contact-rich Tasks by Combining Soft and Rigid Robotics with Imitation Learning—0
Quality Diversity Imitation Learning—0
FürElise: Capturing and Physically Synthesizing Hand Motions of Piano Performance—0
Diffusion Imitation from Observation—0
HE-Drive: Human-Like End-to-End Driving with Vision Language Models—0
Active Multi-task Policy Fine-tuningCode0
On the Sample Complexity of a Policy Gradient Algorithm with Occupancy Approximation for General Utility Reinforcement Learning—0
Robust Offline Imitation Learning from Diverse Auxiliary Data—0
Latent Action Priors for Locomotion with Deep Reinforcement Learning—0
Online Control-Informed Learning—0
SEAL: SEmantic-Augmented Imitation Learning via Language Model—0
Effective Tuning Strategies for Generalist Robot Manipulation Policies—0
CANVAS: Commonsense-Aware Navigation System for Intuitive Human-Robot Interaction—0
Improved Sample Complexity of Imitation Learning for Barrier Model Predictive Control—0
Learning to Build by Building Your Own InstructionsCode0
M2Distill: Multi-Modal Distillation for Lifelong Imitation Learning—0
Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning—0
Learning Strategy Representation for Imitation Learning in Multi-Agent Games—0
Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning—0
Good Data Is All Imitation Learning Needs—0
Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models—0
Learning Diverse Robot Striking Motions with Diffusion Models and Kinematically Constrained Gradient Guidance—0
CANDERE-COACH: Reinforcement Learning from Noisy Feedback—0
Whole-Body Teleoperation for Mobile Manipulation at Zero Added Cost—0
RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning—0
Generalized Robot Learning Framework—0
IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition—0
DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control—0
InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation—0
A Perspective on AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems—0
Interactive incremental learning of generalizable skills with local trajectory modulationCode0
An Analysis of Logit Learning with the r-Lambert Function—0
The Prevalence of Neural Collapse in Neural Multivariate Regression—0
Hybrid Imitation-Learning Motion Planner for Urban Driving—0
Imitating Language via Scalable Inverse Reinforcement Learning—0
Preference-Based Multi-Agent Reinforcement Learning: Data Coverage and Algorithmic Techniques—0
FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation LearningCode0
Reinforcement Learning without Human Feedback for Last Mile Fine-Tuning of Large Language Models—0
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games—0
Pareto Inverse Reinforcement Learning for Diverse Expert Policy Generation—0
Automating Deformable Gasket Assembly—0
ACE: A Cross-Platform Visual-Exoskeletons System for Low-Cost Dexterous Teleoperation—0
Optimizing Interpretable Decision Tree Policies for Reinforcement LearningCode0
RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands—0
Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation Learning—0
A Comparison of Imitation Learning Algorithms for Bimanual Manipulation—0
Personalized Dynamic Difficulty Adjustment -- Imitation Learning Meets Reinforcement LearningCode0
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