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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 801–850 of 2122 papers

TitleStatusHype
DIDA: Denoised Imitation Learning based on Domain Adaptation—0
SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring—0
Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid—0
Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics—0
RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation—0
Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization—0
LORD: Large Models based Opposite Reward Design for Autonomous Driving—0
Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning PoliciesCode0
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic SimulationCode0
Imitating Cost-Constrained Behaviors in Reinforcement LearningCode0
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations—0
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination—0
IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History—0
Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling—0
Automated Feature Selection for Inverse Reinforcement Learning—0
Rethinking Adversarial Inverse Reinforcement Learning: Policy Imitation, Transferable Reward Recovery and Algebraic Equilibrium ProofCode0
Information-Theoretic Distillation for Reference-less Summarization—0
Augmented Reality Demonstrations for Scalable Robot Imitation Learning—0
Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types—0
What AIs are not Learning (and Why)—0
Offline Imitation of Badminton Player Behavior via Experiential Contexts and Brownian Motion—0
AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents—0
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight—0
VITaL Pretraining: Visuo-Tactile Pretraining for Tactile and Non-Tactile Manipulation Policies—0
Supervised Fine-Tuning as Inverse Reinforcement Learning—0
SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy—0
DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation—0
TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation—0
Physics-informed Neural Motion Planning on Constraint Manifolds—0
Efficient Data Collection for Robotic Manipulation via Compositional Generalization—0
Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation—0
RT-H: Action Hierarchies Using Language—0
Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL)—0
Robust Policy Learning via Offline Skill Diffusion—0
PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning—0
Imitation Learning Datasets: A Toolkit For Creating Datasets, Training Agents and Benchmarking—0
DIGIC: Domain Generalizable Imitation Learning by Causal Discovery—0
Learning with Language-Guided State Abstractions—0
ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games—0
Imitation-regularized Optimal Transport on Networks: Provable Robustness and Application to Logistics Planning—0
Rethinking Mutual Information for Language Conditioned Skill Discovery on Imitation Learning—0
Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning—0
Learning Translations: Emergent Communication Pretraining for Cooperative Language Acquisition—0
Expressive Whole-Body Control for Humanoid Robots—0
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory—0
BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay—0
CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation—0
Path Planning based on 2D Object Bounding-box—0
DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models—0
SPRINQL: Sub-optimal Demonstrations driven Offline Imitation LearningCode0
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