Zobrazeno 1 - 10
of 217
pro vyhledávání: '"Browne, Will"'
Autor:
van Oort, Tjeard, Miller, Dimity, Browne, Will N., Marticorena, Nicolas, Haviland, Jesse, Suenderhauf, Niko
Many robotic applications require to grasp objects not arbitrarily but at a very specific object part. This is especially important for manipulation tasks beyond simple pick-and-place scenarios or in robot-human interactions, such as object handovers
Externí odkaz:
http://arxiv.org/abs/2406.05951
Deep-learning and large scale language-image training have produced image object detectors that generalise well to diverse environments and semantic classes. However, single-image object detectors trained on internet data are not optimally tailored f
Externí odkaz:
http://arxiv.org/abs/2402.03721
Compensating for slip and skid is crucial for mobile robots navigating outdoor terrains. In these challenging environments, slipping and skidding introduce uncertainties into trajectory tracking systems, potentially compromising the safety of the veh
Externí odkaz:
http://arxiv.org/abs/2309.08863
Interest in reinforcement learning (RL) has recently surged due to the application of deep learning techniques, but these connectionist approaches are opaque compared with symbolic systems. Learning Classifier Systems (LCSs) are evolutionary machine
Externí odkaz:
http://arxiv.org/abs/2305.09945
Reinforcement learning (RL) is experiencing a resurgence in research interest, where Learning Classifier Systems (LCSs) have been applied for many years. However, traditional Michigan approaches tend to evolve large rule bases that are difficult to i
Externí odkaz:
http://arxiv.org/abs/2305.09922
Publikováno v:
IEEE Robotics and Automation Letters, vol. 8, no. 8, pp. 5084-5091, Aug. 2023
Unsupervised Domain Adaptive Object Detection (UDA-OD) uses unlabelled data to improve the reliability of robotic vision systems in open-world environments. Previous approaches to UDA-OD based on self-training have been effective in overcoming change
Externí odkaz:
http://arxiv.org/abs/2302.06039
Lateralization is ubiquitous in vertebrate brains which, as well as its role in locomotion, is considered an important factor in biological intelligence. Lateralization has been associated with both poor and good performance. It has been hypothesized
Externí odkaz:
http://arxiv.org/abs/2302.01542
The majority of computer vision algorithms fail to find higher-order (abstract) patterns in an image so are not robust against adversarial attacks, unlike human lateralized vision. Deep learning considers each input pixel in a homogeneous manner such
Externí odkaz:
http://arxiv.org/abs/2301.12637
Learning classifier systems (LCSs) originated from cognitive-science research but migrated such that LCS became powerful classification techniques. Modern LCSs can be used to extract building blocks of knowledge to solve more difficult problems in th
Externí odkaz:
http://arxiv.org/abs/2006.01415
Multitask Learning is a learning paradigm that deals with multiple different tasks in parallel and transfers knowledge among them. XOF, a Learning Classifier System using tree-based programs to encode building blocks (meta-features), constructs and c
Externí odkaz:
http://arxiv.org/abs/2005.03947