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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 151–175 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
Is Your Imitation Learning Policy Better than Mine? Policy Comparison with Near-Optimal Stopping—0
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—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
LUMOS: Language-Conditioned Imitation Learning with World Models—0
Finetuning Generative Trajectory Model with Reinforcement Learning from Human Feedback—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
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
V-Max: A Reinforcement Learning Framework for Autonomous DrivingCode2
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
PointVLA: Injecting the 3D World into Vision-Language-Action ModelsCode4
One-Shot Dual-Arm Imitation Learning—0
EPR-GAIL: An EPR-Enhanced Hierarchical Imitation Learning Framework to Simulate Complex User Consumption Behaviors—0
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