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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 726–750 of 2122 papers

TitleStatusHype
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents—0
Efficient Fusion and Task Guided Embedding for End-to-end Autonomous Driving—0
Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning—0
Safe CoR: A Dual-Expert Approach to Integrating Imitation Learning and Safe Reinforcement Learning Using Constraint Rewards—0
Open-TeleVision: Teleoperation with Immersive Active Visual Feedback—0
EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning—0
On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents—0
ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning—0
OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents—0
The State-Action-Reward-State-Action Algorithm in Spatial Prisoner's Dilemma Game—0
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention—0
RaCIL: Ray Tracing based Multi-UAV Obstacle Avoidance through Composite Imitation Learning—0
Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging—0
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy—0
Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks—0
CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics—0
Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert DemonstrationsCode0
Offline Imitation Learning with Model-based Reverse Augmentation—0
Physics-informed Imitative Reinforcement Learning for Real-world Driving—0
Visually Robust Adversarial Imitation Learning from Videos with Contrastive LearningCode0
PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner—0
Bridging the Communication Gap: Artificial Agents Learning Sign Language through Imitation—0
BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic ManipulationCode0
Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation—0
CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving—0
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