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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 451–475 of 2122 papers

TitleStatusHype
Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models—0
Empirical Analysis of Sim-and-Real Cotraining Of Diffusion Policies For Planar Pushing from Pixels—0
Robust Offline Imitation Learning Through State-level Trajectory Stitching—0
Task Tokens: A Flexible Approach to Adapting Behavior Foundation Models—0
OminiAdapt: Learning Cross-Task Invariance for Robust and Environment-Aware Robotic Manipulation—0
Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning—0
Bridging the Sim-to-real Gap: A Control Framework for Imitation Learning of Model Predictive Control—0
Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation—0
TamedPUMA: safe and stable imitation learning with geometric fabrics—0
BEAC: Imitating Complex Exploration and Task-oriented Behaviors for Invisible Object Nonprehensile Manipulation—0
End-to-end Sketch-Guided Path Planning through Imitation Learning for Autonomous Mobile RobotsCode0
JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse—0
Denoising-based Contractive Imitation LearningCode0
RoboFactory: Exploring Embodied Agent Collaboration with Compositional Constraints—0
Learning 3D Scene Analogies with Neural Contextual Scene Maps—0
StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion—0
Robotic Paper Wrapping by Learning Force Control—0
CCDP: Composition of Conditional Diffusion Policies with Guided Sampling—0
Online Imitation Learning for Manipulation via Decaying Relative Correction through Teleoperation—0
Learning Bimanual Manipulation via Action Chunking and Inter-Arm Coordination with Transformers—0
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots—0
Quantization-Free Autoregressive Action TransformerCode0
Efficient Imitation under Misspecification—0
HybridGen: VLM-Guided Hybrid Planning for Scalable Data Generation of Imitation Learning—0
FLEX: A Framework for Learning Robot-Agnostic Force-based Skills Involving Sustained Contact Object Manipulation—0
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