Results 311 to 320 of about 5,721,941 (348)

One-Shot Imitation Learning: A Pose Estimation Perspective

Conference on Robot Learning, 2023
In this paper, we study imitation learning under the challenging setting of: (1) only a single demonstration, (2) no further data collection, and (3) no prior task or object knowledge.
Pietro Vitiello, Kamil Dreczkowski, Edward Johns   +2 more
semanticscholar   +1 more source

DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning

IEEE International Conference on Robotics and Automation
Imitation learning from human demonstrations is an effective means to teach robots manipulation skills. But data acquisition is a major bottleneck in applying this paradigm more broadly, due to the high costs and human efforts involved.
Zhenyu Jiang, Yuqi Xie, Kevin Lin, Zhenjia Xu, Weikang Wan, A. Mandlekar, L. Fan, Yuke Zhu   +7 more
semanticscholar   +1 more source

PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving

arXiv.org
We present PLUTO, a powerful framework that pushes the limit of imitation learning-based planning for autonomous driving. Our improvements stem from three pivotal aspects: a longitudinal-lateral aware model architecture that enables flexible and diverse ...
Jie Cheng, Yingbing Chen, Qifeng Chen
semanticscholar   +1 more source

Satellite Observation and Data-Transmission Scheduling Using Imitation Learning Based on Mixed Integer Linear Programming

IEEE Transactions on Aerospace and Electronic Systems, 2023
The Earth observation satellites (EOSs) scheduling problem is generally considered as a complex combinatorial optimization problem due to various technical constraints. It is significant to develop efficient computational frameworks to solve this problem.
Qingyu Qu, Kexin Liu, Xijun Li, Yunfan Zhou, Jinhu Lü   +4 more
semanticscholar   +1 more source

Imitation: Learning and communication

2000
This paper focuses on our works on imitation in autonomous robots. In a first part, we take into account recent studies in the field of developmental psychology and consider the two functions of imitation (learning and communication) that these studies have stressed.
Andry, Pierre, Moga, Sorin, Gaussier, Philippe, Revel, Arnaud, Nadel, Jacqueline   +4 more
openaire   +2 more sources

Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics

Robotics: Science and Systems
We show that off-the-shelf text-based Transformers, with no additional training, can perform few-shot in-context visual imitation learning, mapping visual observations to action sequences that emulate the demonstrator's behaviour.
Norman Di Palo, Edward Johns
semanticscholar   +1 more source

Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks

Conference on Robot Learning
We explore whether surgical manipulation tasks can be learned on the da Vinci robot via imitation learning. However, the da Vinci system presents unique challenges which hinder straight-forward implementation of imitation learning.
Ji Woong Kim, Tony Zhao, Samuel Schmidgall, A. Deguet, Marin Kobilarov, Chelsea Finn, Axel Krieger   +6 more
semanticscholar   +1 more source

DexMV: Imitation Learning for Dexterous Manipulation from Human Videos

European Conference on Computer Vision, 2021
. While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation.
Yuzhe Qin, Yueh-Hua Wu, Shaowei Liu, Hanwen Jiang, Ruihan Yang, Yang Fu, Xiaolong Wang   +6 more
semanticscholar   +1 more source

Towards Diverse Behaviors: A Benchmark for Imitation Learning with Human Demonstrations

International Conference on Learning Representations
Imitation learning with human data has demonstrated remarkable success in teaching robots in a wide range of skills. However, the inherent diversity in human behavior leads to the emergence of multi-modal data distributions, thereby presenting a ...
Xiaogang Jia, Denis Blessing, Xinkai Jiang, Moritz Reuss, Atalay Donat, Rudolf Lioutikov, Gerhard Neumann   +6 more
semanticscholar   +1 more source

ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation

IEEE International Conference on Robotics and Automation
In most contact-rich manipulation tasks, humans apply time-varying forces to the target object, compensating for inaccuracies in the vision-guided hand trajectory.
Wenhai Liu, Junbo Wang, Yiming Wang, Weiming Wang, Cewu Lu   +4 more
semanticscholar   +1 more source