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检索条件"机构=Robotics and Machine Learning"

共 450 条记录，以下是91-100 订阅

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Smartphone interruptibility using density-weighted uncertainty sampling with reinforcement learning

Smartphone interruptibility using density-weighted uncertain...

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10th International Conference on machine learning and Applications, ICMLA 2011

作者： Fisher, Robert Simmons, Reid Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213-3815 United States Robotics Institute Carnegie Mellon University Pittsburgh PA 15213-3815 United States

ISBN: (纸本)9780769546070

We present the In-Context application for smart-phones, which combines signal processing, active learning, and reinforcement learning to autonomously create a personalized model of interruptibility for incoming phone calls. We empirically evaluate the system, and show that we can obtain an average of 96.12% classification accuracy when predicting interruptibility after a week of training. In contrast to previous work, we leverage density-weighted uncertainty sampling combined with a reinforcement learning framework applied to passively collected data to achieve comparable or superior classification accuracy using many fewer queries issued to the user. © 2011 IEEE.

关键词： Reinforcement learning

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Integrated motor control, planning, grasping and high-level reasoning in a blocks world using probabilistic inference

Integrated motor control, planning, grasping and high-level ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Marc Toussaint Nils Plath Tobias Lang Nikolay Jetchev Machine Learning and Robotics Group Technical University Berlin Berlin Germany

ISBN: (纸本)9781424450381;9781424450404

A new approach to planning and goal-directed behavior has recently been proposed using probabilistic inference in a graphical model that represents states, actions, constraints and goals of the future to infer appropriate actions and controls. The approach has led to new algorithms on the control and trajectory optimization level as well as for high-level rule-based planning in relational domains. In this paper we integrate these methods to a coherent control, trajectory optimization, and action planning architecture, using the principle of planning by inference across all levels of abstractions. Our scenario is a real blocks world: using a 14DoF Schunk arm and hand with tactile sensors and a stereo camera, the goal is to manipulate a set of objects on the table in a goal-oriented way. For high-level reasoning, we learn relational rule-based models from experience in simulation.

关键词： Motor drives Robot sensing systems Graphical models Trajectory Inference algorithms Tactile sensors Robot vision systems Cameras Decision making Information processing

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Temporal collaborative filtering with Bayesian probabilistic tensor factorization

Temporal collaborative filtering with Bayesian probabilistic...

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10th SIAM International Conference on Data Mining, SDM 2010

作者： Xiong, Liang Chen, Xi Huang, Tzu-Kuo Schneider, Jeff Carbonell, Jaime G. Machine Learning Department Carnegie Mellon University United States Robotics Institute Carnegie Mellon University United States Language Technology Institute Carnegie Mellon University United States

Real-world relational data are seldom stationary, yet traditional collaborative filtering algorithms generally rely on this assumption. Motivated by our sales prediction problem, we propose a factor-based algorithm that is able to take time into account. By introducing additional factors for time, we formalize this problem as a tensor factorization with a special constraint on the time dimension. Further, we provide a fully Bayesian treatment to avoid tuning parameters and achieve automatic model complexity control. To learn the model we develop an efficient sampling procedure that is capable of analyzing large-scale data sets. This new algorithm, called Bayesian Probabilistic Tensor Factorization (BPTF), is evaluated on several real-world problems including sales prediction and movie recommendation. Empirical results demonstrate the superiority of our temporal model. Copyright © by SIAM.

关键词： Collaborative filtering

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Source-level analysis of brain activity in the process of learning and retrieving new information 5

Source-level analysis of brain activity in the process of le...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Makarov, Vladimir Grubov, Vadim Kurkin, Semen Smirnov, Nikita Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

In this paper, we discovered the increase in EEG power in the theta frequency range in the occipital cortex during a visual analysis of new information and the power decrease in the alpha range in the temporal lobe during retaining information. The revealed increase in theta power correlates with the response time and errors rate. The alpha power correlates with response time. Summarizing the above, the detected cluster of theta-activity during information perception may reflect the increased concentration of attention. © 2021 IEEE

关键词： Electroencephalography

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Effect of prehistory on the ambiguous stimuli processing in the human brain 5

Effect of prehistory on the ambiguous stimuli processing in ...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Kuc, Alexander Neuroscience and Cognitive Technology Lab Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

To model the picture of the external environment, the brain uses data coming from the sensory system. However, it is believed that the brain’s representation of the external environment is formed not only by sensory information, but also by a priori knowledge, the so-called predictions. This work considers how the previous sensory information (sensory prehistory) influences the processing of the current visual stimuli. Bistable images of the Necker cube with varying degrees of ambiguity were used as visual stimuli. We found EEG biomarkers possibly reflecting the effect of prehistory on the brain state and the ongoing stimulus processing. © 2021 IEEE

关键词： Visualization Complex networks Biomarkers Brain modeling Electroencephalography Data models Time factors

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Quasi-movements as an intermediate type of motion 5

Quasi-movements as an intermediate type of motion

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Smirnov, Nikita M. Kurkin, Semen Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

This paper discusses the three distinct types of motor activity, namely quasi, real, and imagery. Quasi-motion is voluntary movements that are minimized to the point that finally become undetectable by objective measures. This type of motion is particular from the point of difficulty of classifying it as real or imaginary. In this paper, we are aiming to show it from the sensor- and source-level perspectives. © 2021 IEEE

关键词： Dynamics Complex networks

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PID-inspired inductive biases for deep reinforcement learning in partially observable control tasks 23

PID-inspired inductive biases for deep reinforcement learnin...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Ian Char Jeff Schneider Machine Learning Department Carnegie Mellon University Pittsburgh PA Machine Learning Department Robotics Institute Carnegie Mellon University Pittsburgh PA

Deep reinforcement learning (RL) has shown immense potential for learning to control systems through data alone. However, one challenge deep RL faces is that the full state of the system is often not observable. When this is the case, the policy needs to leverage the history of observations to infer the current state. At the same time, differences between the training and testing environments makes it critical for the policy not to overfit to the sequence of observations it sees at training time. As such, there is an important balancing act between having the history encoder be flexible enough to extract relevant information, yet be robust to changes in the environment. To strike this balance, we look to the PID controller for inspiration. We assert the PID controller's success shows that only summing and differencing are needed to accumulate information over time for many control tasks. Following this principle, we propose two architectures for encoding history: one that directly uses PID features and another that extends these core ideas and can be used in arbitrary control tasks. When compared with prior approaches, our encoders produce policies that are often more robust and achieve better performance on a variety of tracking tasks. Going beyond tracking tasks, our policies achieve 1.7x better performance on average over previous state-of-the-art methods on a suite of locomotion control tasks. Code available at https://***/IanChar/GPIDE

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Biomarkers of brain activity for the performance evaluation in the process of solving cognitive tasks 5

Biomarkers of brain activity for the performance evaluation ...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Badarin, Artem Smirnov, Nikita Kurkin, Semen Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

In this paper, we analyze the brain activity during the execution by the subject of simple cognitive tasks associated with visual attention and symbol perception. We obtain biomarkers of brain activity in the process of solving cognitive tasks. These biomarkers make it possible to relate the quality and speed of execution of the tasks offered to the subject with the brain’s activity of the cortical network. We use a combination of time-frequency and statistical analysis to calculate activity characteristics. The results obtained can be used to develop neural interfaces for training attention and adjusting the learning process. © 2021 IEEE

关键词： Biomarkers

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learning Demands for Ride-pooling Services: A Case Study in Berlin

Learning Demands for Ride-pooling Services: A Case Study in ...

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IEEE Symposium on Intelligent Vehicle

作者： Martin Aleksandrov Dahlem Center for Machine Learning and Robotics Freie Universitä Berlin Berlin Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

We design a survey-and-scenario-based method for investigating the demand of real people for ride-pooling services in Berlin. We deploy it among public transport users to study where, why, how, and when they would use ride-pooling. In particular, we learn about their socio-demographic features, trip purposes, and deciding factors, as well as their preferences for service waiting times and fares. Our results inform service operators about dispatching decisions, allow for encoding user behavior in utility functions for reasoning, and enable the design of fair pricing schemes to improve service accessibility.

关键词： Legged locomotion Shared transport Intelligent vehicles Design methodology Pricing Encoding Dispatching

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Comparison on brain activity for mechanical imagery and execution: a brief review 5

Comparison on brain activity for mechanical imagery and exec...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Chepurova, Alla Kurkin, Semen Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

With this review we summarize the current state of scientific studies in the field of MI (motor imagery) and ME (motor execution). We composed brain map and description which correlate different brain areas with type of movements it is responsible for. That gives a more complete and systematic picture of human brain functionality in a case of ME and MI. Also we found the research gap and possible improvements for the further researches in this field. © 2021 IEEE

关键词： Brain

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