Supervisory teleoperation with online learning and optimal control
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In this paper, we propose a method for modeling trajectory patterns with both regional and velocity observations through the probabilistic topic model. By embedding Gaussian models into the discrete topic model framework, our method uses continuous velocit ...
While the affordances of face-to-face and online environments have been studied somewhat extensively, there is relatively less research on how technology-mediated learning takes place across multiple media in the networked classroom environment where face- ...
We examine the problem of learning a set of parameters from a distributed dataset. We assume the datasets are collected by agents over a distributed ad-hoc network, and that the communication of the actual raw data is prohibitive due to either privacy cons ...
Teacher orchestration of technology-enhanced learning (TEL) processes plays a major role in students' outcomes, especially in face-to-face classrooms. However, few studies look into the fine-grained details of how such orchestration unfolds, the challenges ...
This paper extends hierarchical task network (HTN) planning with lightweight learning, considering that in robotics, actions have a non-zero probability of failing. Our work applies to A*-based HTN planners with lifting. We prove that the planner finds the ...
We examine the performance of stochastic-gradient learners over connected networks for global optimization problems involving risk functions that are not necessarily quadratic. We consider two well-studied classes of distributed schemes including consensus ...
We propose a fully-distributed stochastic-gradient strategy based on diffusion adaptation techniques. We show that, for strongly convex risk functions, the excess-risk at every node decays at the rate of O(1/Ni), where N is the number of learners and i is ...
From e-commerce to social networking sites, recommender systems are gaining more and more interest. They provide connections, news, resources, or products of interest. This paper presents a federated recommender system, which exploits data from different o ...
Open ended learning is a dynamic process based on the continuous analysis of new data, guided by past experience. On one side it is helpful to take advantage of prior knowledge when only few information on a new task is available (transfer learning). On th ...
Neurological patients with impaired upper limbs often receive arm therapy to restore or relearn lost motor functions. During the last years robotic devices were developed to assist the patient during the training. In daily life the diversity of movements i ...