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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 1401–1450 of 2122 papers

TitleStatusHype
Towards Learning to Imitate from a Single Video Demonstration—0
Visual Imitation Learning with Recurrent Siamese Networks—0
Visual Imitation Made Easy—0
Visual Imitation with a Minimal Adversary—0
Visual Semantic Planning using Deep Successor Representations—0
Visuospatial Skill Learning for Robots—0
VITaL Pretraining: Visuo-Tactile Pretraining for Tactile and Non-Tactile Manipulation Policies—0
VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation—0
Wasserstein Adversarial Imitation Learning—0
Watch and Match: Supercharging Imitation with Regularized Optimal Transport—0
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward—0
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards—0
WayEx: Waypoint Exploration using a Single Demonstration—0
Waypoint-Based Imitation Learning for Robotic Manipulation—0
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models—0
Weighted Maximum Entropy Inverse Reinforcement Learning—0
What AIs are not Learning (and Why)—0
What data do we need for training an AV motion planner?—0
What is the Reward for Handwriting? -- Handwriting Generation by Imitation Learning—0
What Makes A Good Fisherman? Linear Regression under Self-Selection Bias—0
What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations—0
What Matters for Adversarial Imitation Learning?—0
What Matters for Batch Online Reinforcement Learning in Robotics?—0
What Matters in Learning from Large-Scale Datasets for Robot Manipulation—0
What Matters to Enhance Traffic Rule Compliance of Imitation Learning for End-to-End Autonomous Driving—0
What's the Move? Hybrid Imitation Learning via Salient Points—0
What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning—0
What Would the Expert do()?: Causal Imitation Learning—0
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?—0
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence—0
Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning—0
WildLMa: Long Horizon Loco-Manipulation in the Wild—0
WoMAP: World Models For Embodied Open-Vocabulary Object Localization—0
World-aware Planning Narratives Enhance Large Vision-Language Model Planner—0
X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real—0
Yordle: An Efficient Imitation Learning for Branch and Bound—0
You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations—0
Whole-Body Teleoperation for Mobile Manipulation at Zero Added Cost—0
Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation—0
Zero-shot Imitation Policy via Search in Demonstration Dataset—0
Zero-shot Task Adaptation using Natural Language—0
Zero-Shot Transfer in Imitation Learning—0
Zero-Shot Visual Generalization in Robot Manipulation—0
Multi-Agent Imitation Learning with Copulas—0
Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation—0
Leveraging Human Guidance for Deep Reinforcement Learning Tasks—0
myGym: Modular Toolkit for Visuomotor Robotic Tasks—0
VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections—0
Play to the Score: Stage-Guided Dynamic Multi-Sensory Fusion for Robotic Manipulation—0
CMR-Agent: Learning a Cross-Modal Agent for Iterative Image-to-Point Cloud Registration—0
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