Imitation Learning

Imitation learning aims to train agents to mimic expert behavior by learning from observational data, primarily focusing on efficiently transferring complex skills from humans or other advanced controllers to robots. Current research emphasizes improving data efficiency through techniques like active learning, data augmentation, and leveraging large language models to provide richer context and handle failures. This field is crucial for advancing robotics, autonomous driving, and other areas requiring complex control policies, as it offers a more data-driven and potentially less labor-intensive approach than traditional programming methods.

Papers

December 7, 2022

Learning Graph Search Heuristics
Michal Pándy, Weikang Qiu, Gabriele Corso, Petar Veličković, Rex Ying, Jure Leskovec, Pietro Liò
Imitation Learning Graph Search Neural Network Heuristic Path Finding Algorithm Heuristic Core

December 6, 2022

Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots
Shamil Mamedov, Rudolf Reiter, Seyed Mahdi Basiri Azad, Joschka Boedecker, Moritz Diehl, Jan Swevers
Imitation Learning Nonlinear Model Predictive Control Safe Human Robot Collaboration Interactive Imitation Learning Flexible Robot Robotic Arm System

December 5, 2022

Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrations
Malte Mosbach, Kara Moraw, Sven Behnke
Reinforcement Learning Imitation Learning Anthropomorphic Robotic Hand GPU Based

December 2, 2022

Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control
Devesh K. Jha, Siddarth Jain, Diego Romeres, William Yerazunis, Daniel Nikovski
Imitation Learning Collaborative Robot Force Control Human Robot Collaborative Assembly

November 30, 2022

Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation
Alain Andres, Esther Villar-Rodriguez, Javier Del Ser
Reinforcement Learning Imitation Learning Sparse Reward Intrinsic Motivation Exploration Performance

November 26, 2022

Transfer RL via the Undo Maps Formalism
Abhi Gupta, Ted Moskovitz, David Alvarez-Melis, Aldo Pacchiano
Reinforcement Learning Imitation Learning Formality Transfer Based Policy Distribution Matching Push Forward

November 21, 2022

Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback
Josh Abramson, Arun Ahuja, Federico Carnevale, Petko Georgiev, Alex Goldin, Alden Hung, Jessica Landon, Jirka Lhotka, Timothy Lillicrap, Alistair Muldal, George Powell, Adam Santoro, Guy Scully, Sanjana Srivastava, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen Yan, Rui Zhu
Reinforcement Learning Imitation Learning Human Feedback Reward Function Multimodal Agent

November 16, 2022

November 14, 2022

Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control
Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari-Trecate
Imitation Learning Optimal Control Regret Minimization Optimal Trajectory Robust Control Low Regret

November 13, 2022

Out-of-Dynamics Imitation Learning from Multimodal Demonstrations
Yiwen Qiu, Jialong Wu, Zhangjie Cao, Mingsheng Long
Imitation Learning Feature Imitation Task Transferability Based Imitation Multimodal Demonstration

November 11, 2022

November 7, 2022

Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation
Hanbit Oh, Hikaru Sasaki, Brendan Michael, Takamitsu Matsubara
Imitation Learning Robot Manipulation Generalist Robot Real Robot Experiment Robust Imitation Disturbance Injection

November 6, 2022

Leveraging Haptic Feedback to Improve Data Quality and Quantity for Deep Imitation Learning Models
Catie Cuan, Allison Okamura, Mohi Khansari
Imitation Learning Robot Teleoperation Data Quality Haptic Feedback Quantity Aware

October 31, 2022

October 30, 2022

Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games
The Viet Bui, Tien Mai, Thanh H. Nguyen
Imitation Learning Two Player Adversarial Policy

October 29, 2022

DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving
Resul Dagdanov, Feyza Eksen, Halil Durmus, Ferhat Yurdakul, Nazim Kemal Ure
Reinforcement Learning Data Detection Imitation Learning Reinforcement Learning Agent Reliable Autonomous Driving Future Failure Autonomous Driving Benchmark

October 22, 2022

Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learning
Azimkhon Ostonov
Deep Reinforcement Learning Imitation Learning 3D Reconstruction Surface Reconstruction 3D Shape Object Reconstruction Cut and Approximate 3D Shape Reconstruction Cross Section

Imitation Learning

Papers

Learning Graph Search Heuristics

Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots

Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrations

Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control

Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation

Transfer RL via the Undo Maps Formalism

Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback

ToolFlowNet: Robotic Manipulation with Tools via Predicting Tool Flow from Point Clouds

Generating Stable and Collision-Free Policies through Lyapunov Function Learning

Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control

Out-of-Dynamics Imitation Learning from Multimodal Demonstrations

NeuroCERIL: Robotic Imitation Learning via Hierarchical Cause-Effect Reasoning in Programmable Attractor Neural Networks

Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction

Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation

Leveraging Haptic Feedback to Improve Data Quality and Quantity for Deep Imitation Learning Models

Interactive Imitation Learning in Robotics: A Survey

Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning

Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games

DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving

Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learning