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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 601–625 of 2122 papers

TitleStatusHype
Unpacking the Individual Components of Diffusion Policy—0
Learning for Long-Horizon Planning via Neuro-Symbolic Abductive ImitationCode0
Prediction with Action: Visual Policy Learning via Joint Denoising Process—0
LHPF: Look back the History and Plan for the Future in Autonomous Driving—0
Self-reconfiguration Strategies for Space-distributed Spacecraft—0
Spatially Visual Perception for End-to-End Robotic Learning—0
RoCoDA: Counterfactual Data Augmentation for Data-Efficient Robot Learning from Demonstrations—0
End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning—0
WildLMa: Long Horizon Loco-Manipulation in the Wild—0
Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning—0
Instant Policy: In-Context Imitation Learning via Graph Diffusion—0
Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms—0
Learning Generalizable 3D Manipulation With 10 DemonstrationsCode0
Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented ImitationCode0
Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment—0
Robot See, Robot Do: Imitation Reward for Noisy Financial Environments—0
Imitation Learning from Observations: An Autoregressive Mixture of Experts Approach—0
Navigation with QPHIL: Quantizing Planner for Hierarchical Implicit Q-Learning—0
EMPERROR: A Flexible Generative Perception Error Model for Probing Self-Driving Planners—0
Learning Memory Mechanisms for Decision Making through DemonstrationsCode0
Imitation from Diverse Behaviors: Wasserstein Quality Diversity Imitation Learning with Single-Step Archive Exploration—0
Identifying Differential Patient Care Through Inverse Intent Inference—0
Scaling Laws for Pre-training Agents and World Models—0
ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policy—0
Object and Contact Point Tracking in Demonstrations Using 3D Gaussian Splatting—0
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