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Martinez-Hernandez, Uriel, Vouloutsi, Vasiliki, Mura, Anna, Mangan, Michael, Asada, Minoru, Prescott, T. J., Verschure, P., (2019). Biomimetic and Biohybrid Systems 8th International Conference, Living Machines 2019, Nara, Japan, July 9–12, 2019, Proceedings , Springer, Cham (Lausanne, Switzerland) 11556, 1-384

This book constitutes the proceedings of the 8th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2019, held in Nara, Japan, in July 2019. The 26 full and 16 short papers presented in this volume were carefully reviewed and selected from 45 submissions. They deal with research on novel life-like technologies inspired by the scientific investigation of biological systems, biomimetics, and research that seeks to interface biological and artificial systems to create biohybrid systems.

Keywords: Artificial intelligence, Biomimetics, Computer architecture, Human robot interaction, Human-Computer Interaction (HCI), Humanoid robot, Image processing, Learning algorithms, Mobile robots, Multipurpose robots, Neural networks, Quadruped robots, Reinforcement learning, Robot learning, Robotics, Robots, Sensor, Sensors, Swarm robotics, User interfaces


Moulin-Frier, C., Fischer, T., Petit, M., Pointeau, G., Puigbo, J., Pattacini, U., Low, S. C., Camilleri, D., Nguyen, P., Hoffmann, M., Chang, H. J., Zambelli, M., Mealier, A., Damianou, A., Metta, G., Prescott, T. J., Demiris, Y., Dominey, P. F., Verschure, P. F. M. J., (2018). DAC-h3: A proactive robot cognitive architecture to acquire and express knowledge about the world and the self IEEE Transactions on Cognitive and Developmental Systems 10, (4), 1005-1022

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.

Keywords: Autobiographical Memory., Biology, Cognition, Cognitive Robotics, Computer architecture, Distributed Adaptive Control, Grounding, Human-Robot Interaction, Humanoid robots, Robot sensing systems, Symbol Grounding


Aviles, A. I., Alsaleh, S. M., Sobrevilla, P., Casals, A., (2015). Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery Engineering in Medicine and Biology Society (EMBC) 37th Annual International Conference of the IEEE , IEEE (Milan, Italy) , 1-4

The lack of force feedback is considered one of the major limitations in Robot Assisted Minimally Invasive Surgeries. Since add-on sensors are not a practical solution for clinical environments, in this paper we present a force estimation approach that starts with the reconstruction of a 3D deformation structure of the tissue surface by minimizing an energy functional. A Recurrent Neural Network-Long Short Term Memory (RNN-LSTM) based architecture is then presented to accurately estimate the applied forces. According to the results, our solution offers long-term stability and shows a significant percentage of accuracy improvement, ranging from about 54% to 78%, over existing approaches.

Keywords: Computer architecture, Estimation, Force, Microprocessors, Robot sensing systems, Surgery