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
Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots
Hongrui Yu, Vineet R. Kamat, Carol C. Menassa
Prompt, Plan, Perform: LLM-based Humanoid Control via Quantized Imitation Learning
Jingkai Sun, Qiang Zhang, Yiqun Duan, Xiaoyang Jiang, Chong Cheng, Renjing Xu
What Matters to Enhance Traffic Rule Compliance of Imitation Learning for End-to-End Autonomous Driving
Hongkuan Zhou, Wei Cao, Aifen Sui, Zhenshan Bing
Imitation Learning-based Visual Servoing for Tracking Moving Objects
Rocco Felici, Matteo Saveriano, Loris Roveda, Antonio Paolillo
Naturalistic Robot Arm Trajectory Generation via Representation Learning
Jayjun Lee, Adam J. Spiers