The Experts below are selected from a list of 94533 Experts worldwide ranked by ideXlab platform
Yonggang Wen - One of the best experts on this subject based on the ideXlab platform.
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energy efficient Task Execution for application as a general topology in mobile cloud computing
IEEE Transactions on Cloud Computing, 2018Co-Authors: Weiwen Zhang, Yonggang WenAbstract:Mobile cloud computing has been proposed as an effective solution to augment the capabilities of resource-poor mobile devices. In this paper, we investigate energy-efficient collaborative Task Execution to reduce the energy consumption on mobile devices. We model a mobile application as a general topology, consisting of a set of fine-grained Tasks. Each Task within the application can be either executed on the mobile device or on the cloud. We aim to find out the Execution decision for each Task to minimize the energy consumption on the mobile device while meeting a delay deadline. We formulate the collaborative Task Execution as a delay-constrained workflow scheduling problem. We leverage the partial critical path analysis for the workflow scheduling; for each path, we schedule the Tasks using two algorithms based on different cases. For the special case without Execution restriction, we adopt one-climb policy to obtain the solution. For the general case where there are some Tasks that must be executed either on the mobile device or on the cloud, we adopt Lagrange Relaxation based Aggregated Cost (LARAC) algorithm to obtain the solution. We show by simulation that the collaborative Task Execution is more energy-efficient than local Execution and remote Execution.
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collaborative Task Execution in mobile cloud computing under a stochastic wireless channel
IEEE Transactions on Wireless Communications, 2015Co-Authors: Weiwen Zhang, Yonggang WenAbstract:This paper investigates collaborative Task Execution between a mobile device and a cloud clone for mobile applications under a stochastic wireless channel. A mobile application is modeled as a sequence of Tasks that can be executed on the mobile device or on the cloud clone. We aim to minimize the energy consumption on the mobile device while meeting a time deadline, by strategically offloading Tasks to the cloud. We formulate the collaborative Task Execution as a constrained shortest path problem. We derive a one-climb policy by characterizing the optimal solution and then propose an enumeration algorithm for the collaborative Task Execution in polynomial time. Further, we apply the LARAC algorithm to solving the optimization problem approximately, which has lower complexity than the enumeration algorithm. Simulation results show that the approximate solution of the LARAC algorithm is close to the optimal solution of the enumeration algorithm. In addition, we consider a probabilistic time deadline, which is transformed to hard deadline by Markov inequality. Moreover, compared to the local Execution and the remote Execution, the collaborative Task Execution can significantly save the energy consumption on the mobile device, prolonging its battery life.
Julius P A Dewald - One of the best experts on this subject based on the ideXlab platform.
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impact of motor Task Execution on an individual s ability to mirror forearm positions
Experimental Brain Research, 2018Co-Authors: Netta Gurari, Justin M Drogos, Shawn Lopez, Julius P A DewaldAbstract:This work is motivated by our goal of determining why individuals with stroke are impaired when locating their arms in space. We assessed the ability of individuals without neurological impairments to mirror their forearms during various motor Tasks so that we could identify baseline performance in an unimpaired population. Nine right-hand dominant participants without neurological impairments mirrored forearm positions bi-directionally (i.e., right forearm mirrors left forearm, vice versa) for three motor Tasks (i.e., passive, passive/active, and active) and two position identification modes (i.e., mirroring to a position stored in working memory versus concurrently felt by the opposite arm). During each trial, the participant’s reference forearm moved to a flexion (\(77.5^\circ\)) or extension (\(102.5^\circ\)) position, and then, their opposite forearm mirrored the position of their reference forearm. The main finding across all tested conditions is that participants mirrored forearm positions with an average magnitude of error \(<\, 6.3^\circ\). When controlling their forearms’ movements (active motor Task), participants mirrored forearm positions more accurately by up to, on average, \(5.7^\circ\) at the flexion location than at the extension location. Moreover, participants mirrored forearm positions more accurately by up to, on average, \(3.5^\circ\) when their forearms were moved for them rather than when they controlled their forearms’ movements. Task directionality and position identification mode did not significantly affect participant arm mirroring accuracy. These findings are relevant for interpreting in future work the reason why impairments occur, on similar Tasks, in individuals with altered motor commands, working memory, and arm impedance, e.g., post-stroke hemiparesis.
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Impact of motor Task Execution on an individual's ability to mirror forearm positions.
Experimental brain research, 2018Co-Authors: Netta Gurari, Justin M Drogos, Shawn Lopez, Julius P A DewaldAbstract:This work is motivated by our goal of determining why individuals with stroke are impaired when locating their arms in space. We assessed the ability of individuals without neurological impairments to mirror their forearms during various motor Tasks so that we could identify baseline performance in an unimpaired population. Nine right-hand dominant participants without neurological impairments mirrored forearm positions bi-directionally (i.e., right forearm mirrors left forearm, vice versa) for three motor Tasks (i.e., passive, passive/active, and active) and two position identification modes (i.e., mirroring to a position stored in working memory versus concurrently felt by the opposite arm). During each trial, the participant's reference forearm moved to a flexion ([Formula: see text]) or extension ([Formula: see text]) position, and then, their opposite forearm mirrored the position of their reference forearm. The main finding across all tested conditions is that participants mirrored forearm positions with an average magnitude of error [Formula: see text]. When controlling their forearms' movements (active motor Task), participants mirrored forearm positions more accurately by up to, on average, [Formula: see text] at the flexion location than at the extension location. Moreover, participants mirrored forearm positions more accurately by up to, on average, [Formula: see text] when their forearms were moved for them rather than when they controlled their forearms' movements. Task directionality and position identification mode did not significantly affect participant arm mirroring accuracy. These findings are relevant for interpreting in future work the reason why impairments occur, on similar Tasks, in individuals with altered motor commands, working memory, and arm impedance, e.g., post-stroke hemiparesis.
Dieter Fox - One of the best experts on this subject based on the ideXlab platform.
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reactive long horizon Task Execution via visual skill and precondition models
arXiv: Robotics, 2020Co-Authors: Shohin Mukherjee, Chris Paxton, Arsalan Mousavian, Adam Fishman, Maxim Likhachev, Dieter FoxAbstract:Zero-shot Execution of unseen robotic Tasks is important to allowing robots to perform a wide variety of Tasks in human environments, but collecting the amounts of data necessary to train end-to-end policies in the real-world is often infeasible. We describe an approach for sim-to-real training that can accomplish unseen robotic Tasks using models learned in simulation to ground components of a simple Task planner. We learn a library of parameterized skills, along with a set of predicates-based preconditions and termination conditions, entirely in simulation. We explore a block-stacking Task because it has a clear structure, where multiple skills must be chained together, but our methods are applicable to a wide range of other problems and domains, and can transfer from simulation to the real-world with no fine tuning. The system is able to recognize failures and accomplish long-horizon Tasks from perceptual input, which is critical for real-world Execution. We evaluate our proposed approach in both simulation and in the real-world, showing an increase in success rate from 91.6% to 98% in simulation and from 10% to 80% success rate in the real-world as compared with naive baselines. For experiment videos including both real-world and simulation, see: https://www.youtube.com/playlist?list=PL-oD0xHUngeLfQmpngYkGFZarstfPOXqX
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transferable Task Execution from pixels through deep planning domain learning
International Conference on Robotics and Automation, 2020Co-Authors: Kei Kase, Chris Paxton, Hammad Mazhar, Tetsuya Ogata, Dieter FoxAbstract:While robots can learn models to solve many manipulation Tasks from raw visual input, they cannot usually use these models to solve new problems. On the other hand, symbolic planning methods such as STRIPS have long been able to solve new problems given only a domain definition and a symbolic goal, but these approaches often struggle on the real world robotic Tasks due to the challenges of grounding these symbols from sensor data in a partially-observable world. We propose Deep Planning Domain Learning (DPDL), an approach that combines the strengths of both methods to learn a hierarchical model. DPDL learns a high-level model which predicts values for a large set of logical predicates consisting of the current symbolic world state, and separately learns a low-level policy which translates symbolic operators into executable actions on the robot. This allows us to perform complex, multistep Tasks even when the robot has not been explicitly trained on them. We show our method on manipulation Tasks in a photorealistic kitchen scenario.
Rohan Paul - One of the best experts on this subject based on the ideXlab platform.
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multimodal estimation and communication of latent semantic knowledge for robust Execution of robot instructions
The International Journal of Robotics Research, 2020Co-Authors: Jacob Arkin, Daehyung Park, Subhro Roy, Matthew R Walter, Nicholas Roy, Thomas M Howard, Rohan PaulAbstract:The goal of this article is to enable robots to perform robust Task Execution following human instructions in partially observable environments. A robot’s ability to interpret and execute commands ...
Weiwen Zhang - One of the best experts on this subject based on the ideXlab platform.
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energy efficient Task Execution for application as a general topology in mobile cloud computing
IEEE Transactions on Cloud Computing, 2018Co-Authors: Weiwen Zhang, Yonggang WenAbstract:Mobile cloud computing has been proposed as an effective solution to augment the capabilities of resource-poor mobile devices. In this paper, we investigate energy-efficient collaborative Task Execution to reduce the energy consumption on mobile devices. We model a mobile application as a general topology, consisting of a set of fine-grained Tasks. Each Task within the application can be either executed on the mobile device or on the cloud. We aim to find out the Execution decision for each Task to minimize the energy consumption on the mobile device while meeting a delay deadline. We formulate the collaborative Task Execution as a delay-constrained workflow scheduling problem. We leverage the partial critical path analysis for the workflow scheduling; for each path, we schedule the Tasks using two algorithms based on different cases. For the special case without Execution restriction, we adopt one-climb policy to obtain the solution. For the general case where there are some Tasks that must be executed either on the mobile device or on the cloud, we adopt Lagrange Relaxation based Aggregated Cost (LARAC) algorithm to obtain the solution. We show by simulation that the collaborative Task Execution is more energy-efficient than local Execution and remote Execution.
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collaborative Task Execution in mobile cloud computing under a stochastic wireless channel
IEEE Transactions on Wireless Communications, 2015Co-Authors: Weiwen Zhang, Yonggang WenAbstract:This paper investigates collaborative Task Execution between a mobile device and a cloud clone for mobile applications under a stochastic wireless channel. A mobile application is modeled as a sequence of Tasks that can be executed on the mobile device or on the cloud clone. We aim to minimize the energy consumption on the mobile device while meeting a time deadline, by strategically offloading Tasks to the cloud. We formulate the collaborative Task Execution as a constrained shortest path problem. We derive a one-climb policy by characterizing the optimal solution and then propose an enumeration algorithm for the collaborative Task Execution in polynomial time. Further, we apply the LARAC algorithm to solving the optimization problem approximately, which has lower complexity than the enumeration algorithm. Simulation results show that the approximate solution of the LARAC algorithm is close to the optimal solution of the enumeration algorithm. In addition, we consider a probabilistic time deadline, which is transformed to hard deadline by Markov inequality. Moreover, compared to the local Execution and the remote Execution, the collaborative Task Execution can significantly save the energy consumption on the mobile device, prolonging its battery life.
