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
Task-Oriented Hierarchical Object Decomposition for Visuomotor Control
Jianing Qian, Yunshuang Li, Bernadette Bucher, Dinesh Jayaraman
GarmentLab: A Unified Simulation and Benchmark for Garment Manipulation
Haoran Lu, Ruihai Wu, Yitong Li, Sijie Li, Ziyu Zhu, Chuanruo Ning, Yan Shen, Longzan Luo, Yuanpei Chen, Hao Dong
Learning to Look Around: Enhancing Teleoperation and Learning with a Human-like Actuated Neck
Bipasha Sen, Michelle Wang, Nandini Thakur, Aditya Agarwal, Pulkit Agrawal
Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
Tian Xu, Zhilong Zhang, Ruishuo Chen, Yihao Sun, Yang Yu
EgoMimic: Scaling Imitation Learning via Egocentric Video
Simar Kareer, Dhruv Patel, Ryan Punamiya, Pranay Mathur, Shuo Cheng, Chen Wang, Judy Hoffman, Danfei Xu
DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning
Zhenyu Jiang, Yuqi Xie, Kevin Lin, Zhenjia Xu, Weikang Wan, Ajay Mandlekar, Linxi Fan, Yuke Zhu
3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing
Binghao Huang, Yixuan Wang, Xinyi Yang, Yiyue Luo, Yunzhu Li
Multi-Robot Pursuit in Parameterized Formation via Imitation Learning
Jinyong Chen, Rui Zhou, Zhaozong Wang, Yunjie Zhang, Guibin Sun
Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding
He Jiang, Yutong Wang, Rishi Veerapaneni, Tanishq Duhan, Guillaume Sartoretti, Jiaoyang Li
Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional Reinforcement Learning
Amr Gomaa, Bilal Mahdy
ARCADE: Scalable Demonstration Collection and Generation via Augmented Reality for Imitation Learning
Yue Yang, Bryce Ikeda, Gedas Bertasius, Daniel Szafir
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning
Hanlin Yang, Jian Yao, Weiming Liu, Qing Wang, Hanmin Qin, Hansheng Kong, Kirk Tang, Jiechao Xiong, Chao Yu, Kai Li, Junliang Xing, Hongwu Chen, Juchao Zhuo, Qiang Fu, Yang Wei, Haobo Fu