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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 151–200 of 2122 papers

TitleStatusHype
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
GenOSIL: Generalized Optimal and Safe Robot Control using Parameter-Conditioned Imitation Learning—0
Residual Policy Gradient: A Reward View of KL-regularized Objective—0
Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketches—0
TASTE-Rob: Advancing Video Generation of Task-Oriented Hand-Object Interaction for Generalizable Robotic Manipulation—0
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—0
Is Your Imitation Learning Policy Better than Mine? Policy Comparison with Near-Optimal Stopping—0
Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation—0
NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models—0
Finetuning Generative Trajectory Model with Reinforcement Learning from Human Feedback—0
LUMOS: Language-Conditioned Imitation Learning with World Models—0
The Pitfalls of Imitation Learning when Actions are Continuous—0
SE(3)-Equivariant Robot Learning and Control: A Tutorial Survey—0
Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface—0
Imitation Learning of Correlated Policies in Stackelberg Games—0
V-Max: A Reinforcement Learning Framework for Autonomous DrivingCode2
TLA: Tactile-Language-Action Model for Contact-Rich Manipulation—0
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning PoliciesCode1
A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous RacingCode0
Policy Regularization on Globally Accessible States in Cross-Dynamics Reinforcement Learning—0
How to Train Your Robots? The Impact of Demonstration Modality on Imitation Learning—0
One-Shot Dual-Arm Imitation Learning—0
PointVLA: Injecting the 3D World into Vision-Language-Action ModelsCode4
EPR-GAIL: An EPR-Enhanced Hierarchical Imitation Learning Framework to Simulate Complex User Consumption Behaviors—0
Learning to Drive by Imitating Surrounding Vehicles—0
On a Connection Between Imitation Learning and RLHFCode1
dARt Vinci: Egocentric Data Collection for Surgical Robot Learning at Scale—0
Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interaction—0
Look Before You Leap: Using Serialized State Machine for Language Conditioned Robotic Manipulation—0
Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning—0
Data-Efficient Learning from Human Interventions for Mobile Robots—0
Enhancing Autonomous Driving Safety with Collision Scenario Integration—0
Curating Demonstrations using Online Experience—0
A2Perf: Real-World Autonomous Agents Benchmark—0
Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich ManipulationCode3
ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation—0
CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving—0
Perceptual Motor Learning with Active Inference Framework for Robust Lateral Control—0
Improving Retrospective Language Agents via Joint Policy Gradient Optimization—0
POPGym Arcade: Parallel Pixelated POMDPsCode1
FABG : End-to-end Imitation Learning for Embodied Affective Human-Robot Interaction—0
CARIL: Confidence-Aware Regression in Imitation Learning for Autonomous DrivingCode0
ProDapt: Proprioceptive Adaptation using Long-term Memory DiffusionCode0
DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping—0
IL-SOAR : Imitation Learning with Soft Optimistic Actor cRitic—0
Fine-Tuning Vision-Language-Action Models: Optimizing Speed and SuccessCode5
RIZE: Regularized Imitation Learning via Distributional Reinforcement LearningCode0
ObjectVLA: End-to-End Open-World Object Manipulation Without Demonstration—0
Task-Driven Semantic Quantization and Imitation Learning for Goal-Oriented Communications—0
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