{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:04:54Z","timestamp":1768565094615,"version":"3.49.0"},"reference-count":24,"publisher":"MIT Press - Journals","issue":"2","license":[{"start":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T00:00:00Z","timestamp":1639612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,1,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Model-based control has great potential for use in real robots due to its high sampling efficiency. Nevertheless, dealing with physical contacts and generating accurate motions are inevitable for practical robot control tasks, such as precise manipulation. For a real-time, model-based approach, the difficulty of contact-rich tasks that requires precise movement lies in the fact that a model needs to accurately predict forthcoming contact events within a limited length of time rather than detect them afterward with sensors. Therefore, in this study, we investigate whether and how neural network models can learn a task-related model useful enough for model-based control, that is, a model predicting future states, including contact events. To this end, we propose a structured neural network model predictive control (SNN-MPC) method, whose neural network architecture is designed with explicit inertia matrix representation. To train the proposed network, we develop a two-stage modeling procedure for contact-rich dynamics from a limited number of samples. As a contact-rich task, we take up a trackball manipulation task using a physical 3-DoF finger robot. The results showed that the SNN-MPC outperformed MPC with a conventional fully connected network model on the manipulation task.<\/jats:p>","DOI":"10.1162\/neco_a_01465","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T22:55:57Z","timestamp":1639695357000},"page":"360-377","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":2,"title":["Implicit Contact Dynamics Modeling With Explicit Inertia Matrix Representation for Real-Time, Model-Based Control in Physical Environment"],"prefix":"10.1162","volume":"34","author":[{"given":"Takeshi D.","family":"Itoh","sequence":"first","affiliation":[{"name":"Department of Brain Robot Interface, ATR Computational Neuroscience Laboratories, Kyoto, 619-0288, Japan, and Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara, 630-0192, Japan itoh.takeshi.ik4@is.naist.jp"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koji","family":"Ishihara","sequence":"additional","affiliation":[{"name":"Department of Brain Robot Interface, ATR Computational Neuroscience Laboratories, Kyoto, 619-0288, Japan ishihara-k@atr.jp"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Morimoto","sequence":"additional","affiliation":[{"name":"Department of Brain Robot Interface, ATR Computational Neuroscience Laboratories, Kyoto, 619-0288, Japan and Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan xmorimo@atr.jp"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"2022040618223907400_B1","first-page":"9558","volume-title":"Advances in neural information processing systems, 32","author":"Agrawal","year":"2019"},{"key":"2022040618223907400_B2","article-title":"Analytical derivatives of rigid body 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