SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 2122 papers

TitleStatusHype
FDPP: Fine-tune Diffusion Policy with Human Preference—0
Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline DataCode0
Generalizable Graph Neural Networks for Robust Power Grid Topology ControlCode0
Motion Tracks: A Unified Representation for Human-Robot Transfer in Few-Shot Imitation Learning—0
Smart Imitator: Learning from Imperfect Clinical DecisionsCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Imitation Learning of MPC with Neural Networks: Error Guarantees and Sparsification—0
SR-Reward: Taking The Path More Traveled—0
DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving—0
Self-Supervised Cross-View Correspondence with Predictive Cycle Consistency—0
Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors—0
The intrinsic motivation of reinforcement and imitation learning for sequential tasks—0
Imitation Learning from Suboptimal Demonstrations via Meta-Learning An Action RankerCode0
Mimicking-Bench: A Benchmark for Generalizable Humanoid-Scene Interaction Learning via Human Mimicking—0
Decoding fairness: a reinforcement learning perspectiveCode0
SORREL: Suboptimal-Demonstration-Guided Reinforcement Learning for Learning to Branch—0
AdaCred: Adaptive Causal Decision Transformers with Feature Crediting—0
Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination—0
Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models—0
Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model—0
RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation—0
Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask LearningCode2
Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)—0
Student-Informed Teacher Training—0
Learning Novel Skills from Language-Generated Demonstrations—0
TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning—0
Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous ControlCode0
Contractive Dynamical Imitation Policies for Efficient Out-of-Sample RecoveryCode0
Swarm Behavior Cloning—0
ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement TasksCode2
A Note on Sample Complexity of Interactive Imitation Learning with Log Loss—0
Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone—0
Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories—0
RLZero: Direct Policy Inference from Language Without In-Domain Supervision—0
What's the Move? Hybrid Imitation Learning via Salient Points—0
DEMO: Reframing Dialogue Interaction with Fine-grained Element ModelingCode1
TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in MinecraftCode1
BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoTCode0
Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed SimulationCode0
Learning on One Mode: Addressing Multi-Modality in Offline Reinforcement LearningCode0
Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning—0
Quantization-Aware Imitation-Learning for Resource-Efficient Robotic Control—0
LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations—0
ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning—0
Global Tensor Motion PlanningCode1
Unpacking the Individual Components of Diffusion Policy—0
G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object ManipulationCode0
Learning for Long-Horizon Planning via Neuro-Symbolic Abductive ImitationCode0
Prediction with Action: Visual Policy Learning via Joint Denoising Process—0
CityWalker: Learning Embodied Urban Navigation from Web-Scale VideosCode3
Show:102550
← PrevPage 6 of 43Next →

No leaderboard results yet.