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
Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective
Wanying Wang, Yichen Zhu, Yirui Zhou, Chaomin Shen, Jian Tang, Zhiyuan Xu, Yaxin Peng, Yangchun Zhang
Aligning Human Intent from Imperfect Demonstrations with Confidence-based Inverse soft-Q Learning
Xizhou Bu, Wenjuan Li, Zhengxiong Liu, Zhiqiang Ma, Panfeng Huang
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges
Noémie Jaquier, Michael C. Welle, Andrej Gams, Kunpeng Yao, Bernardo Fichera, Aude Billard, Aleš Ude, Tamim Asfour, Danica Kragic
Toward a Surgeon-in-the-Loop Ophthalmic Robotic Apprentice using Reinforcement and Imitation Learning
Amr Gomaa, Bilal Mahdy, Niko Kleer, Antonio Krüger