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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 1001–1025 of 2122 papers

TitleStatusHype
CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations—0
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?—0
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies—0
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning—0
Hitting time for Markov decision process—0
Learning Dynamic-Objective Policies from a Class of Optimal Trajectories—0
Learning Agile Robotic Locomotion Skills by Imitating Animals—0
Learning a Safety Verifiable Adaptive Cruise Controller from Human Driving Data—0
Diverse Imitation Learning via Self-Organizing Generative Models—0
JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse—0
EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning—0
EPR-GAIL: An EPR-Enhanced Hierarchical Imitation Learning Framework to Simulate Complex User Consumption Behaviors—0
Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interaction—0
Keyframe-Focused Visual Imitation Learning—0
Keypoint Abstraction using Large Models for Object-Relative Imitation Learning—0
Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics—0
CIVIL: Causal and Intuitive Visual Imitation Learning—0
Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation—0
KinTwin: Imitation Learning with Torque and Muscle Driven Biomechanical Models Enables Precise Replication of Able-Bodied and Impaired Movement from Markerless Motion Capture—0
Knowledge Distillation for Mobile Edge Computation Offloading—0
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation—0
Lagrangian Generative Adversarial Imitation Learning with Safety—0
ENTL: Embodied Navigation Trajectory Learner—0
CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving—0
Entity-Centric Coreference Resolution with Model Stacking—0
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