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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 351–400 of 2122 papers

TitleStatusHype
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
Touch begins where vision ends: Generalizable policies for contact-rich manipulation—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
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
From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models—0
Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation—0
Towards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning—0
UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation—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
BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning—0
FlowOE: Imitation Learning with Flow Policy from Ensemble RL Experts for Optimal Execution under Heston Volatility and Concave Market Impacts—0
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
RoboTransfer: Geometry-Consistent Video Diffusion for Robotic Visual Policy Transfer—0
SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning—0
Learning Compositional Behaviors from Demonstration and Language—0
Streaming Flow Policy: Simplifying diffusion/flow-matching policies by treating action trajectories as flow trajectories—0
Object-Centric Action-Enhanced Representations for Robot Visuo-Motor Policy Learning—0
Spatial RoboGrasp: Generalized Robotic Grasping Control Policy—0
Inverse Q-Learning Done Right: Offline Imitation Learning in Q^π-Realizable MDPsCode0
Online Knowledge Distillation with Reward Guidance—0
MisoDICE: Multi-Agent Imitation from Unlabeled Mixed-Quality Demonstrations—0
Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space—0
Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy—0
ProgRM: Build Better GUI Agents with Progress Rewards—0
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)—0
Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only—0
Reinforcement Twinning for Hybrid Control of Flapping-Wing Drones—0
Guided Policy Optimization under Partial ObservabilityCode0
Learning-based Autonomous Oversteer Control and Collision Avoidance—0
UAV-Flow Colosseo: A Real-World Benchmark for Flying-on-a-Word UAV Imitation Learning—0
FLARE: Robot Learning with Implicit World Modeling—0
Structured Agent Distillation for Large Language Model—0
Imitation Learning via Focused Satisficing—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
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