In this chapter we present results of our ongoing research on efficient and fluent human-robot collaboration that is heavily inspired by recent experimental findings about the neurocognitive mechanisms supporting joint action in humans. The robot control architecture implements the joint coordination of actions and goals as a dynamic process that integrates contextual cues, shared task knowledge and the predicted outcome of the user’s motor behavior. The architecture is formalized as a coupled system of dynamic neural fields representing a distributed network of local but connected neural populations with specific functionalities. We validate the approach in a task in which a robot and a human user jointly construct a toy ‘vehicle’. We show that the context-dependent mapping from action observation onto appropriate complementary actions allows the robot to cope with dynamically changing joint action situations. More specifically, the results illustrate crucial cognitive capacities for efficient and successful human-robot collaboration such as goal inference, error detection and anticipatory action selection.
Bicho, E., W. Erlhagen, E. Sousa, L. Louro, N. Hipolito, E. C. Silva, R. Silva, F. Ferreira, T. Machado, M. Hulstijn, Y. Maas, E. de Bruijn, R. H. Cuijpers, R. Newman-Norlund, H. van Schie, R.G.J. Meulenbroek & H. Bekkering
2012. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, ► pp. 5458 ff.
Costa e Silva, Eliana, Fernanda Costa, Estela Bicho & Wolfram Erlhagen
2011. Nonlinear Optimization for Human-Like Movements of a High Degree of Freedom Robotics Arm-Hand System. In Computational Science and Its Applications - ICCSA 2011 [Lecture Notes in Computer Science, 6784], ► pp. 327 ff.
Erlhagen, Wolfram & Estela Bicho
2014. A Dynamic Neural Field Approach to Natural and Efficient Human-Robot Collaboration. In Neural Fields, ► pp. 341 ff.
Franklin, Stan, Tamas Madl, Sidney D'Mello & Javier Snaider
2014. LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning. IEEE Transactions on Autonomous Mental Development 6:1 ► pp. 19 ff.
Gulletta, Gianpaolo, Eliana Costa e Silva, Wolfram Erlhagen, Ruud Meulenbroek, Maria Fernanda Pires Costa & Estela Bicho
2021. A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots. International Journal of Advanced Robotic Systems 18:2 ► pp. 172988142199858 ff.
Humphries, Jacqueline, Pepijn Van de Ven & Alan Ryan
2023. Augmenting the Human in Industry 4.0 to Add Value: A Taxonomy of Human Augmentation Approach. In Computer-Human Interaction Research and Applications [Communications in Computer and Information Science, 1996], ► pp. 318 ff.
Malheiro, Tiago, Estela Bicho, Toni Machado, Luis Louro, Sergio Monteiro, Paulo Vicente & Wolfram Erlhagen
2017. 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), ► pp. 146 ff.
2016. Combining intention and emotional state inference in a dynamic neural field architecture for human-robot joint action. Adaptive Behavior 24:5 ► pp. 350 ff.
2021. A neural integrator model for planning and value-based decision making of a robotics assistant. Neural Computing and Applications 33:8 ► pp. 3737 ff.
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