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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 1–50 of 2122 papers

TitleStatusHype
The Imitation Game: Turing Machine Imitator is Length Generalizable Reasoner—0
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)—0
Fast Bilateral Teleoperation and Imitation Learning Using Sensorless Force Control via Accurate Dynamics Model—0
LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving—0
EC-Flow: Enabling Versatile Robotic Manipulation from Action-Unlabeled Videos via Embodiment-Centric Flow—0
Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-TuningCode2
World-aware Planning Narratives Enhance Large Vision-Language Model Planner—0
Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration—0
Ark: An Open-source Python-based Framework for Robot Learning—0
CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity—0
Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining—0
Learning Task-Agnostic Skill Bases to Uncover Motor Primitives in Animal Behaviors—0
Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation—0
Steering Robots with Inference-Time Interactions—0
A Survey on Imitation Learning for Contact-Rich Tasks in Robotics—0
What Matters in Learning from Large-Scale Datasets for Robot Manipulation—0
Touch begins where vision ends: Generalizable policies for contact-rich manipulation—0
Adapting by Analogy: OOD Generalization of Visuomotor Policies via Functional Correspondence—0
SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies—0
Human-Robot Navigation using Event-based Cameras and Reinforcement Learning—0
Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation—0
From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models—0
Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning EnvironmentCode2
UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation—0
Robot-Gated Interactive Imitation Learning with Adaptive Intervention MechanismCode1
Towards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning—0
Reinforcement Learning via Implicit Imitation Guidance—0
ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving—0
SpikePingpong: High-Frequency Spike Vision-based Robot Learning for Precise Striking in Table Tennis Game—0
FlowOE: Imitation Learning with Flow Policy from Ensemble RL Experts for Optimal Execution under Heston Volatility and Concave Market Impacts—0
BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning—0
A Smooth Sea Never Made a Skilled SAILOR: Robust Imitation via Learning to SearchCode2
Advancing Tool-Augmented Large Language Models via Meta-Verification and Reflection LearningCode1
Learning dissection trajectories from expert surgical videos via imitation learning with equivariant diffusion—0
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous DrivingCode0
SGN-CIRL: Scene Graph-based Navigation with Curriculum, Imitation, and Reinforcement LearningCode0
Rodrigues Network for Learning Robot Actions—0
WoMAP: World Models For Embodied Open-Vocabulary Object Localization—0
Variational Adaptive Noise and Dropout towards Stable Recurrent Neural Networks—0
Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents—0
Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey—0
Enhanced DACER Algorithm with High Diffusion Efficiency—0
Normalizing Flows are Capable Models for RLCode1
RoboTransfer: Geometry-Consistent Video Diffusion for Robotic Visual Policy Transfer—0
ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained KnowledgeCode1
Streaming Flow Policy: Simplifying diffusion/flow-matching policies by treating action trajectories as flow trajectories—0
SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning—0
Learning Compositional Behaviors from Demonstration and Language—0
Spatial RoboGrasp: Generalized Robotic Grasping Control Policy—0
Object-Centric Action-Enhanced Representations for Robot Visuo-Motor Policy Learning—0
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