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IEEE International Conference on Robotics and Automation (ICRA)

作者： Kazuki Shibata Tomohiko Jimbo Takamitsu Matsubara Autonomous Distributed Cooperative Control Program Data Analytics Research-Domain Toyota Central R&D Labs. Inc. Japan Graduate School of Science and Technology Division of Information Science Nara Institute of Science and Technology Nara Japan

In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-end deep neural network policies may be insufficient for covering communication and control; these methods cannot decide the timing of communication and can only work with fixed-rate communications. Therefore, our framework exploits event-triggered architecture, namely, a feedback controller that computes the communication input and a triggering mechanism that determines when the input has to be updated again. Such event-triggered control policies are efficiently optimized using a multi-agent deep deterministic policy gradient. We confirmed that our approach could balance the transport performance and communication savings through numerical simulations.

关键词： Deep learning Automation Conferences Reinforcement learning Computer architecture Numerical simulation Timing

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Robust shape estimation with false-positive contact detection

arXiv

引用

arXiv 2021年

作者： Shibata, Kazuki Miyano, Tatsuya Jimbo, Tomohiko Matsubara, Takamitsu Autonomous Distributed Cooperative Control Program Data Analytics Research-Domain Toyota Central RandD Labs. Inc. 41-1 Yokomichi Nagakute Aichi Japan Graduate School of Science and Technology Division of Information Science Nara Institute of Science and Technology Nara Japan

We propose a means of omni-directional contact detection using accelerometers instead of tactile sensors for object shape estimation using touch. Unlike tactile sensors, our contact-based detection method tends to induce a degree of uncertainty with false-positive contact data because the sensors may react not only to actual contact but also to the unstable behavior of the robot. Therefore, it is crucial to consider a robust shape estimation method capable of handling such false-positive contact data. To realize this, we introduce the concept of heteroscedasticity into the contact data and propose a robust shape estimation algorithm based on Gaussian process implicit surfaces (GPIS). We confirmed that our algorithm not only reduces shape estimation errors caused by false-positive contact data but also distinguishes false-positive contact data more clearly than the GPIS through simulations and actual experiments using a quadcopter. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport

arXiv

引用

arXiv 2021年

作者： Shibata, Kazuki Jimbo, Tomohiko Matsubara, Takamitsu Autonomous Distributed Cooperative Control Program Data Analytics Research-Domain Toyota Central R&D Labs. Inc. 41-1 Yokomichi Nagakute Aichi Japan Graduate School of Science and Technology Division of Information Science Nara Institute of Science and Technology Nara Japan

In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-end deep neural network policies may be insufficient for covering communication and control;these methods cannot decide the timing of communication and can only work with fixed-rate communications. Therefore, our framework exploits event-triggered architecture, namely, a feedback controller that computes the communication input and a triggering mechanism that determines when the input has to be updated again. Such event-triggered control policies are efficiently optimized using a multi-agent deep deterministic policy gradient. We confirmed that our approach could balance the transport performance and communication savings through numerical simulations. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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