Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Weigend, Fabian"'
Effective human-robot collaboration hinges on robust communication channels, with visual signaling playing a pivotal role due to its intuitive appeal. Yet, the creation of visually intuitive cues often demands extensive resources and specialized know
Externí odkaz:
http://arxiv.org/abs/2409.13927
We present an open-source library for seamless robot control through motion capture using smartphones and smartwatches. Our library features three modes: Watch Only Mode, enabling control with a single smartwatch; Upper Arm Mode, offering heightened
Externí odkaz:
http://arxiv.org/abs/2406.01117
While imitation learning provides a simple and effective framework for policy learning, acquiring consistent actions during robot execution remains a challenging task. Existing approaches primarily focus on either modifying the action representation
Externí odkaz:
http://arxiv.org/abs/2404.12539
Autor:
Weigend, Fabian C, Liu, Xiao, Sonawani, Shubham, Kumar, Neelesh, Vasudevan, Venugopal, Amor, Heni Ben
This paper introduces iRoCo (intuitive Robot Control) - a framework for ubiquitous human-robot collaboration using a single smartwatch and smartphone. By integrating probabilistic differentiable filters, iRoCo optimizes a combination of precise robot
Externí odkaz:
http://arxiv.org/abs/2403.07199
Ubiquitous robot control and human-robot collaboration using smart devices poses a challenging problem primarily due to strict accuracy requirements and sparse information. This paper presents a novel approach that incorporates a probabilistic differ
Externí odkaz:
http://arxiv.org/abs/2309.06606
This work devises an optimized machine learning approach for human arm pose estimation from a single smartwatch. Our approach results in a distribution of possible wrist and elbow positions, which allows for a measure of uncertainty and the detection
Externí odkaz:
http://arxiv.org/abs/2306.13192
Purpose: Performance models are important tools for coaches and athletes to optimise competition outcomes or training schedules. A recently published hydraulic performance model has been reported to outperform established work-balance models in predi
Externí odkaz:
http://arxiv.org/abs/2207.14295
Data Science advances in sports commonly involve "big data", i.e., large sport-related data sets. However, such big data sets are not always available, necessitating specialized models that apply to relatively few observations. One important area of
Externí odkaz:
http://arxiv.org/abs/2108.04510
This work proposes to use evolutionary computation as a pathway to allow a new perspective on the modeling of energy expenditure and recovery of an individual athlete during exercise. We revisit a theoretical concept called the "three component hydra
Externí odkaz:
http://arxiv.org/abs/2104.07903
Autor:
Weigend, Fabian C.1 (AUTHOR) Fabian.Weigend@westernsydney.edu.au, Clarke, David C.2 (AUTHOR), Obst, Oliver3 (AUTHOR), Siegler, Jason4 (AUTHOR)
Publikováno v:
Annals of Operations Research. Jun2023, Vol. 325 Issue 1, p589-613. 25p.
