Results 31 to 40 of about 96,046 (256)

Hand-Object Interaction: From Human Demonstrations to Robot Manipulation

Frontiers in Robotics and AI, 2021
Human-object interaction is of great relevance for robots to operate in human environments. However, state-of-the-art robotic hands are far from replicating humans skills. It is, therefore, essential to study how humans use their hands to develop similar
Alessandro Carfì, Timothy Patten, Yingyi Kuang, Ali Hammoud, Mohamad Alameh, Elisa Maiettini, Abraham Itzhak Weinberg, Diego Faria, Fulvio Mastrogiovanni, Guillem Alenyà, Lorenzo Natale, Véronique Perdereau, Markus Vincze, Aude Billard   +13 more
doaj   +1 more source

Machine Teaching for Human Inverse Reinforcement Learning

Frontiers in Robotics and AI, 2021
As robots continue to acquire useful skills, their ability to teach their expertise will provide humans the two-fold benefit of learning from robots and collaborating fluently with them.
Michael S. Lee, Henny Admoni, Reid Simmons   +2 more
doaj   +1 more source

Review of Research on Robot Programming by Learning from Demonstration

Jisuanji kexue yu tansuo, 2020
The traditional industrial robot programming method puts forward higher requirements for the programming level of employees, and the programming cycle is long, which is difficult to meet the production requirements of multiple varieties, small batch and ...
YIN Congcong, ZHANG Qiuju
doaj   +1 more source

Research on Robot Screwing Skill Method Based on Demonstration Learning

Sensors, 2023
A robot screwing skill learning framework based on teaching–learning is proposed to improve the generalization ability of robots for different scenarios and objects, combined with the experience of a human operation.
Fengming Li, Yunfeng Bai, Man Zhao, Tianyu Fu, Yu Men, Rui Song   +5 more
doaj   +1 more source

Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

IEEE Access, 2021
The optical motion capture (MoCap) sensor provides an effective way to capture human motions and transform them into valuable data that can be applied to certain tasks, e.g. robot learning from demonstration (LfD).
Haopeng Hu, Zhiqi Cao, Xiansheng Yang, Hao Xiong, Yunjiang Lou   +4 more
doaj   +1 more source

Predictive Learning from Demonstration

, 2011
A model-free learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated in a robot Learning from Demonstration (LFD) setting. PSL is inspired by several functional models of the brain. It constructs sequences of predictable sensory-motor patterns, without relying on predefined higher-level concepts.
Erik Alexander Billing, Thomas Hellström, Lars-Erik Janlert   +2 more
openaire   +3 more sources

Learning to control from expert demonstrations

CoRR, 2022
In this paper, we revisit the problem of learning a stabilizing controller from a finite number of demonstrations by an expert. By first focusing on feedback linearizable systems, we show how to combine expert demonstrations into a stabilizing controller, provided that demonstrations are sufficiently long and there are at least $n+1$ of them, where $n$
Alimzhan Sultangazin, Luigi Pannocchi, Lucas Fraile, Paulo Tabuada   +3 more
openaire   +2 more sources

Learning Post-Stroke Gait Training Strategies by Modeling Patient-Therapist Interaction

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023
For safe and effective robot-aided gait training, it is essential to incorporate the knowledge and expertise of physical therapists. Toward this goal, we directly learn from physical therapists’ demonstrations of manual gait assistance in stroke ...
Seyed Mostafa Rezayat Sorkhabadi, Mason Smith, Roozbeh Khodmbashi, Rachel Lopez, Melissa Raasch, Trent Maruyama, Christina Kwasnica, Wenlong Zhang   +7 more
doaj   +1 more source

Learning to Grasp from a Single Demonstration

CoRR, 2018
Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world. We propose a simpler learning-from-demonstration approach that is able to detect the object to grasp from merely a single ...
Van Molle, Pieter, Verbelen, Tim, De Coninck, Elias, De Boom, Cedric, Simoens, Pieter, Dhoedt, Bart   +5 more
openaire   +3 more sources

Gamma-Regression-Based Inverse Reinforcement Learning From Suboptimal Demonstrations

IEEE Access
Inverse reinforcement learning (IRL) is a technique that estimates the intention of an expert who acts optimally on a specific intention, as a reward from demonstration (i.e., recorded data of the expert’s behavior). Traditional IRL algorithms are
Daiko Kishikawa, Sachiyo Arai
doaj   +1 more source