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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 626–650 of 2122 papers

TitleStatusHype
Out-of-Distribution Recovery with Object-Centric Keypoint Inverse Policy for Visuomotor Imitation Learning—0
So You Think You Can Scale Up Autonomous Robot Data Collection?—0
Efficient Active Imitation Learning with Random Network Distillation—0
Safe Imitation Learning-based Optimal Energy Storage Systems Dispatch in Distribution Networks—0
DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning—0
State- and context-dependent robotic manipulation and grasping via uncertainty-aware imitation learning—0
Rethinking Inverse Reinforcement Learning: from Data Alignment to Task AlignmentCode0
3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing—0
Keypoint Abstraction using Large Models for Object-Relative Imitation Learning—0
SoftCTRL: Soft conservative KL-control of Transformer Reinforcement Learning for Autonomous Driving—0
Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation—0
Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning—0
Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding—0
Identifying Selections for Unsupervised Subtask Discovery—0
Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional Reinforcement Learning—0
GHIL-Glue: Hierarchical Control with Filtered Subgoal Images—0
MILES: Making Imitation Learning Easy with Self-Supervision—0
SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment—0
SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulation—0
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning—0
Latent Weight Diffusion: Generating reactive policies instead of trajectories—0
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning—0
ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis—0
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning PerspectiveCode0
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback—0
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