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

May 21, 2024

One-Shot Imitation Learning with Invariance Matching for Robotic Manipulation
Xinyu Zhang, Abdeslam Boularias
Imitation Learning Robotics Domain Robotic Manipulation Video Demonstration Shot Imitation Robot Demonstration Various Invariance Based Algorithm

May 16, 2024

Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning
Francisco Leiva, Javier Ruiz-del-Solar
Reinforcement Learning Imitation Learning Mobile Robot Reinforcement Learning Algorithm Behavior Cloning Abnormal Signal Modulation Scheme Local Planning

May 13, 2024

May 7, 2024

Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
Chunlin Tian, Zhan Shi, Xinpeng Qin, Li Li, Chengzhong Xu
Federated Learning Imitation Learning Client Selection Pairwise Learning Training Convergence Device Selection

May 6, 2024

Robotic Constrained Imitation Learning for the Peg Transfer Task in Fundamentals of Laparoscopic Surgery
Kento Kawaharazuka, Kei Okada, Masayuki Inaba
Robot Person Imitation Learning Autonomous Robot Basic Concept Robotic Assisted Surgery Laparoscopic Surgery

May 3, 2024

Imitation Learning in Discounted Linear MDPs without exploration assumptions
Luca Viano, Stratis Skoulakis, Volkan Cevher
Imitation Learning Feature Imitation Efficient Exploration Linear Function Approximation Adversarial Loss Finite Horizon Linear MDPs

May 2, 2024

April 30, 2024

April 29, 2024

April 26, 2024

Beyond Imitation: A Life-long Policy Learning Framework for Path Tracking Control of Autonomous Driving
C. Gong, C. Lu, Z. Li, Z. Liu, J. Gong, X. Chen
Autonomous Driving Imitation Learning Learning Based Feature Imitation Lifelong Reinforcement Learning Path Following Control

April 25, 2024

April 22, 2024

LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots
Dongge Han, Trevor McInroe, Adam Jelley, Stefano V. Albrecht, Peter Bell, Amos Storkey
Large Language Model Imitation Learning Self Training Human Preference LLM Personalization Cleaning Robot LLM Planning

April 20, 2024

Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories
Hamidreza Mirkhani, Behzad Khamidehi, Kasra Rezaee
Imitation Learning High Similarity Feature Imitation Expert Trajectory Safety Critical Driving Scenario Trajectory Augmentation

Imitation Learning

Papers

One-Shot Imitation Learning with Invariance Matching for Robotic Manipulation

Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning

Decision Mamba Architectures

Reducing Risk for Assistive Reinforcement Learning Policies with Diffusion Models

Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning

Robotic Constrained Imitation Learning for the Peg Transfer Task in Fundamentals of Laparoscopic Surgery

Imitation Learning in Discounted Linear MDPs without exploration assumptions

CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning

IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning

Continual Imitation Learning for Prosthetic Limbs

Guiding Attention in End-to-End Driving Models

A Survey of Imitation Learning Methods, Environments and Metrics

Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models

MRIC: Model-Based Reinforcement-Imitation Learning with Mixture-of-Codebooks for Autonomous Driving Simulation

Beyond Imitation: A Life-long Policy Learning Framework for Path Tracking Control of Autonomous Driving

IDIL: Imitation Learning of Intent-Driven Expert Behavior

Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods

Leveraging Pretrained Latent Representations for Few-Shot Imitation Learning on a Dexterous Robotic Hand

LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots

Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories