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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 1201–1225 of 2122 papers

TitleStatusHype
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning—0
Single-Reset Divide & Conquer Imitation Learning—0
Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning—0
Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch—0
Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketches—0
SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning—0
SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment—0
SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation—0
SMART: Self-supervised Multi-task pretrAining with contRol Transformers—0
Smooth Imitation Learning via Smooth Costs and Smooth Policies—0
On the Sample Complexity of Imitation Learning for Smoothed Model Predictive Control—0
Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation—0
Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills—0
Social Motion Prediction with Cognitive Hierarchies—0
SoftCTRL: Soft conservative KL-control of Transformer Reinforcement Learning for Autonomous Driving—0
SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching—0
SORREL: Suboptimal-Demonstration-Guided Reinforcement Learning for Learning to Branch—0
So You Think You Can Scale Up Autonomous Robot Data Collection?—0
SparseDice: Imitation Learning for Temporally Sparse Data via Regularization—0
Spatially Visual Perception for End-to-End Robotic Learning—0
Spatial RoboGrasp: Generalized Robotic Grasping Control Policy—0
Spatial-Temporal Deep Embedding for Vehicle Trajectory Reconstruction from High-Angle Video—0
Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation—0
SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks—0
Specification-Guided Data Aggregation for Semantically Aware Imitation Learning—0
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