The robot is uploading the data …
Meera, A. A., Novicky, F., Parr, T., Friston, K., Lanillos, P., & Sajid, N. (2022). Reclaiming saliency: rhythmic precision-modulated action and perception. Frontiers in Neurorobotics. arXiv preprint arXiv:2203.12652.
Maselli, A., Lanillos, P., & Pezzulo, G. (2022). Active inference unifies intentional and conflict-resolution imperatives of motor control. Plos Computational Biology.
Da Costa, L., Lanillos, P., Sajid, N., Friston, K., & Khan, S. (2022). How active inference could help revolutionise robotics. Entropy, 24(3), 361.
Lanillos, P., Meo, C., Pezzato, C., Meera, A. A., Baioumy, M., Ohata, W., … & Tani, J. (2021). Active Inference in Robotics and Artificial Agents: Survey and Challenges. Under review arXiv preprint arXiv:2112.01871.
Lanillos, P., Franklin, S., Maselli, A., & Franklin, D. W. (2021). Active strategies for multisensory conflict suppression in the virtual hand illusion. Scientific reports, 11(1), 1-14.
Hübotter, J. F., Lanillos, P., & Tomczak, J. M. (2021). Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization. arXiv preprint arXiv:2109.11045.
Meo, C., Franzese, G., Pezzato, C., Spahn, M., & Lanillos, P. (2021). Adaptation through prediction: multisensory active inference torque control. IEEE Trans. Cog. Dev. Sys.
Meo, C., & Lanillos, P. (2021, March). Multimodal vae active inference controller. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2693-2699). IEEE.
Lanillos, P., & van Gerven, M. (2021). Neuroscience-inspired perception-action in robotics: applying active inference for state estimation, control and self-perception. Brain2ai WS @ ICLR
Oliver, G., Lanillos, P., & Cheng, G. (2021). An empirical study of active inference on a humanoid robot. IEEE Transactions on Cognitive and Developmental Systems. [pub][preprint][code][video] https://doi.org/10.1109/TCDS.2021.3049907
Hoffmann, M., Wang, S., Outrata, V., Alzueta, E., & Lanillos, P. (2021). Robot in the mirror: toward an embodied computational model of mirror self-recognition. German Journal of Artificial Intelligence – Künstliche Intelligenz. [pub][preprint][video]
Sancaktar, C., van Gerven, M. A., & Lanillos, P. (2020, October). End-to-end pixel-based deep active inference for body perception and action. In 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp. 1-8). [pub][preprint][code]
Rood, T., van Gerven, M., & Lanillos, P. (2020, September). A deep active inference model of the rubber-hand illusion. In International Workshop on Active Inference (pp. 84-91). Springer, Cham. [pub][code]
Lanillos, P., Franklin, S., & Franklin, D. W. (2020). The predictive brain in action: Involuntary actions reduce body prediction errors. bioRxiv. link
Lanillos, P., Pages, J., Cheng, G. (2020). Robot self/other distinction: active inference meets neural networks learning in a mirror. European Conference on Artificial Intelligence (ECAI 2020). link
Lanillos, P., Oliva, D, Philippsen, A., Nagai, Y., Cheng, G. (2019). A Review on Neural Network Models of Schizophrenia and Autism Spectrum Disorder. Neural Networks. 122, 338-363. https://doi.org/10.1016/j.neunet.2019.10.014
Rasouli, A., Lanillos, P., Cheng, G., & Tsotsos, J. K. (2019). Attention-based Active Visual Search for Mobile Robots. Autonomous Robots, 44, 131–146. https://doi.org/10.1007/s10514-019-09882-z
Oliver, G., Lanillos, P., & Cheng, G. (2019). Active inference body perception and action for humanoid robots. arXiv preprint arXiv:1906.03022.
Deistler, M., Yener, Y., Bergner, F., Lanillos, P., Cheng, G. (2019). Tactile Hallucinations on Artificial Skin Induced by Homeostasis in a Deep Boltzmann Machine. In IEEE International Conference on Cyborg and Bionic Systems 2019.
Braun, J. F., Díez-Valencia, G., Ehrlich, S. K., Lanillos, P., & Cheng, G. (2019). A prototype of a P300 based brain-robot interface to enable multi-modal interaction for patients with limited mobility. In IEEE International Conference on Cyborg and Bionic Systems 2019.
Tayeb, Z., Jakovleski, P., Chen, Z., Lippert, J., Lanillos, P., Lee, D., Cheng, G.(2019). “Enabling the sense of touch and hand gesture decoding using vibrotactile stimulation and EMG signal for prosthetic hands control”. 9th International IEEE EMBS Neural Engineering Conference.
Lanillos, P., & Cheng, G. (2018). Adaptive robot body learning and estimation through predictive coding. IEEE International Conference on Intelligent Robots and Systems (IROS 2018). Best cognitive robotics paper finalist award.
Hinz, N. A., Lanillos, P., Mueller, H., & Cheng, G. (2018). Drifting perceptual patterns suggest prediction errors fusion rather than hypothesis selection: replicating the rubber-hand illusion on a robot. IEEE international conference on development and learning and on epigenetic robotics (ICDL-Epirob 2018).
Diez-Valencia G., Ohashi T., Lanillos, P., Cheng,G (2018) Sensorimotor learning for artificial body perception. Workshop on Crossmodal Learning for Intelligent Robotics. IEEE Int. Conference on Intelligent Robots and Systems (IROS 2018)
Lanillos, P., Cheng, G. (2018) Active inference with function learning for robot body perception. Workshop on Continual Unsupervised Sensorimotor Learning. IEEE Int. conference on development and learning and on epigenetic robotics (ICDL-Epirob 2018).
Lanillos, P., Cheng, G. (2018) Active Vision, Attention, and Learning in robotics. Workshop on Active Vision, Attention, and Learning. IEEE international conference on development and learning and on epigenetic robotics (ICDL-Epirob 2018).
Kaboli, M., Feng, D., Yao, K., Lanillos, P., Cheng, G. (2017) A tactile-based framework for active object learning and discrimination using multi-modal robotic skin, 2017, IEEE Robotics and Automation Letters (RA-L), vol. PP, no. 99, pp. 1–1, 2017.
Lanillos, P., Emannuel-Dean, L., Cheng, G. (2017) Enactive Self: a study of engineering perspectives to obtain the sensorimotor self through enaction. 7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob). Accepted.
Lanillos, P., Ferreira, J. F., & Dias, J. (2017). A Bayesian Hierarchy for Robust Gaze Estimation in Human-Robot Interaction. International Journal of Approximate Reasoning.
Lanillos, P., Emannuel-Dean, L., Cheng, G. (2016) Yielding self-perception in robots through sensorimotor contingencies. IEEE Transactions on Cognitive and Developmental Systems.
Lanillos, P., Cheng, G. (2016), Robots with self-perception: objects discovery and scene disambiguation using visual, proprioceptive and tactile cues correlation during interaction. Poster at Robotics in the 21st century: Challenges and Promises International Workshop (funded by the Volkswagen Foundation and the HeKKSaGOn Network). Sept 2016.
Lanillos, P., Emannuel-Dean, L., Cheng, G. (2016), Multisensory Object Discovery via Self-detection and Artificial Attention. IEEE Int. Conf. on Developmental Learning and Epigenetic Robotics (ICDL-EpiRob). Sept 2016. Best paper presentation distinction award
Dianov, I., Ramirez-Amaro, K., Lanillos, P., Dean-Leon, E., Bergner, F., Cheng, G. Extracting general task structures to accelerate the learning of new tasks. IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids 2016).
Emmanuel-Dean, L., Ramirez-Amaro, K., Bergner, F., Dianov, I., Lanillos, P. and Cheng, G. (2016): Robotic technologies for fast deployment of industrial robot systems. IEEE Industrial Electronics Conference (IECON). Oct 2016.
Oliveira, B., Lanillos, P., Ferreira, J.F. (2016): Gaze Tracing in a Bounded Log-spherical Space for Artificial Attention Systems. Robot 2015, Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, Springer International Publishing, 418, 407-419.
Lanillos, P., Ferreira, J. F., Dias, J. (2015): Designing an Artificial Attention System for Social Robots. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, IEEE.
Ferreira, J. F., Lanillos, P., and Dias, J. (2015): Fast exact Bayesian inference for high-dimensional models. In Workshop on Unconventional computing for Bayesian inference in Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, IEEE.
Lanillos, P., Ferreira, J. F., & Dias, J. (2015): Multisensory 3D saliency for artificial attention systems. In 3rd Workshop on Recognition and Action for Scene Understanding (REACTS), 16th International Conference of Computer Analysis of Images and Patterns (CAIP), 1-6.
Lanillos, P., Ferreira, J. F., Dias, J. (2015): Designing Social Interaction with Robots – Towards a Top-Down Articial Attention Systems. In 1st workshop on improving the quality of life in the elderly using robotic assistive technology: benefits, limitations and challenges, 7th International Conference on Social Robotics (ICSR 2015).
Lanillos, P., Gan, S. K., Besada-Portas, E., Pajares, G., Sukkarieh, S. (2014): Multi-UAV target search using decentralized gradient-based negotiation with expected observation. Information Sciences, 282, 92-110. [IF: 4.038, Q1]
Lanillos, P., Besada-Portas, E., Lopez-Orozco, J. A., de la Cruz, J. M. (2014): Minimum time search in uncertain dynamic domains with complex sensorial platforms. Sensors, 14(8), 14131-14179. [IF: 2.245, Q1].
Lanillos, P., Ferreira, J. F., & Dias, J. (2014): Evaluating the Influence of Automatic Attentional Mechanisms in Human-Robot Interaction. In Workshop: a bridge between Robotics and Neuroscience Workshop in Human-Robot Interaction, 9th ACM/IEEE International Conference on, Bielefeld, Germany.
Lanillos, P. (2013): Minimum time search of moving targets in uncertain environments. Ph.D. Dissertation, Universidad Complutense de Madrid.
Lanillos, P., Yañez-Zuluaga, J., Ruz, J. J., Besada-Portas, E. (2013): A bayesian approach for constrained multi-agent minimum time search in uncertain dynamic domains. In Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference. ACM. 391-398.
Lanillos, P., Besada-Portas, E., Pajares, G., Ruz, J. J. (2012): Minimum time search for lost targets using cross entropy optimization. In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on (pp. 602-609), IEEE.
Besada-Portas, E., Lopez-Orozco, J. A., Lanillos, P., de la Cruz, J. M. (2012): Localization of Non-Linearly Modeled Autonomous Mobile Robots Using Out-of-Sequence Measurements. Sensors, 12(3), 2487-2518. [IF: 2.245, Q1].
Lanillos, P., Ruz, J. J., Pajares, G., de la Cruz, J. M. (2009): Environmental surface boundary tracking and description using a UAV with vision. In Emerging Technologies & Factory Automation (ETFA), 2009. IEEE Conference on, 1-4.
Pajares Martinsanz, G., Ruz Ortíz, J. J., Lanillos, P., Guijarro Mata-García, M., Santos Peñas, M. (2008): Trajectory generation and decision making for UAVs. Revista Iberoamericana de Automática e Informática Industrial, 5(1), 83. [IF: 0.291, Q4].
I am currently a PI and Assistant Professor in Cognitive AI within the Artificial Cognitive Intelligence Group, the at Donders Institute for Brain, Cognition and Behaviour. I am coordinator of the international projects: Spikeference.eu (HBP), DeepSelf and Metatool EU pathfinder challenge. My current main research interests are Neuroscience-inspired AI, Robot Learning, Active Inference, Machine Learning, Body perception and Human-robot interaction.
Previously I was a Marie Sklodowska Curie Senior Fellow (EU H2020) at the Institute of Cognitive Systems (ICS) of the Technische Universität München, leaded by Prof. Gordon Cheng, working for my funded project: SELFCEPTION
I received the M.Sc. degree in computer sciences and the Ph.D. degree in computer engineering, specialization robotics (July 2013), on the subject "minimum time search of mobile targets in uncertain environments" from Complutense University of Madrid (UCM), Spain. In 2006 I joined the Department of Computer Architecture and Automatic Control, UCM, and as a PhD candidate, I have been a visiting researcher at the Aerospace Control Laboratory (ACL), Massachusetts Institute of Technology; the Artificial Intelligence Laboratory (LIA), Ecole Polytechnique Federale de Lausanne; and the Australian Centre for Field Robotics (ACFR), University of Sydney. From 2012 to 2013 I have also been working as a researcher assistant in the optical department of the Physics faculty (UCM) developing machine learning algorithms for handwritten text recognition. I have been a member of the Spanish Government funded projects: "System for surveillance, search and rescue in the sea by means of the collaboration of autonomous marine and air vehicles" and "Planning, simulation and control platform for multiple aerial and marine vehicles cooperation".
Afterwards, I was a postdoctoral researcher at the Artificial Perception team at the Institute of Systems and Robotics (ISR) of the University of Coimbra under CASIR project: Coordinated Control of Stimulus-Driven and Goal-Directed Multisensory Attention within the Context of Social Interaction with Robots and advised by Prof. João Filipe Ferreira and Prof. Jorge Dias.
2021 - Current
Coordinator of Spikeference and DeepSelf projects
Donders Institute for Brain, Cognition and Behaviour. Radboud University. Nijmegen, the Netherlands.
Nov 2019 - Current
Assistant Professor in Cognitive Artificial Intelligence
Donders Institute for Brain, Cognition and Behaviour. Radboud University. Nijmegen, the Netherlands.
2017 - 2019
Principal investigator. Marie S.Curie H2020.
Project selfception.eu Institute for Cognitive Systems. Technical University of Munich
2015 - 2016
Technical University Foundation Fellow
Institute for Cognitive Systems. Technical University of Munich
2013 - 2015
Institute of Systems and Robotics, University of Coimbra, Portugal
PhD Artificial Intelligence.
Summa Cum Laude. Mathworks award. Computer Engineering, Complutense University of Madrid, Spain
MSc. Computer Science Research.
Systems Engineering, Control, Automatics and Robotics. Complutense University of Madrid, Spain. Internship at the Massachusetts Institute of Technology
Complutense University of Madrid
Postdoc on brain-inspired deep learning for robots
The Donders Institute is looking for an early career postdoc on brain-inspired deep learning for robots for the international project DEEPSELF (3 years), funded by the German Research Foundation (DFG), between the Donders Institute (Netherlands, supervision: Pablo Lanillos) and Tübingen University (Germany, supervision: Martin Butz). DEEPSELF is a project that lies between machine learning and cognitive science to investigate the emergence of agency in artificial entities by learning hierarchical predictive encodings of events.
Interested contact p.lanillos[at]donders[dot]ru[dot]nl
Agency appears to enable us to learn, discern and anticipate the consequences of our actions. What should be the internal representation that the agent should learn to plan in different temporal scales? how we can enable robots to answer ‘Did I do it?’ and use that information to interact with the world?
The postdoc will be in charge of developing new brain-inspired machine learning models to allow a robot to learn hierarchical event codes from sensorimotor experience and plan future actions using these event codes. This abilities will be tested in robotic experiments to investigate the emergence of Agency on three levels of abstraction based on human science findings.
The selected candidate will work in an international project team in close collaboration with two PhD students and the principal investigators. The candidate will join an exciting and vibrant young team of experts in machine learning, artificial intelligence and robotics, as well as, be part of a big community of ~20 interdisciplinary research projects under the Active Self DFG priority program umbrella. Thus, the candidate will have the opportunity to take leadership responsibilities and considerably increase the research network. Furthermore, the candidate will participate in summer schools and events organized by the priority program.
Keywords: Hierarchical Deep Learning, Event Segmentation Theory, Active Inference, Robot learning, Agency.
We strongly encourage submissions from different representative minorities.
- A PhD in Artificial Intelligence, Machine Learning, Computational Neuroscience or Robotics
- Experience in probabilistic deep learning
- Good mathematical skills
- Knowledge in any of the following topics: active inference, predictive coding, state representation learning, reinforcement learning, control as inference
- Previous work with robots
- Cognitive science experience
The successful candidate will join the Donders Institute for Brain, Cognition and Behaviour and the department of artificial intelligence at Radboud University in the Netherlands. You will have the opportunity to collaborate and interact with renowned experts in multiple fields and benefit from an exciting growing environment around the European Lab for Learning and Intelligent Systems (ELLIS) Nijmegen unit.
The Donders Institute for Brain, Cognition and Behaviour is a world-class interfaculty research centre, that houses more than 700 researchers devoted to understanding the mechanistic underpinnings of the human mind. Research at the Donders Institute is focused on four themes: Language and communication, Perception, action and control, Plasticity and memory, and Neural computation and Neurotechnology. The Donders Institute has been assessed by an international evaluation committee as excellent and recognized as a ‘very stimulating environment for top researchers, as well as for young talent’. The Donders Institute fosters a collaborative, multi-disciplinary, supportive research environment with a diverse international staff. English is the lingua franca at the Institute.
The Department of Artificial Intelligence focuses on the development of human-like AI systems and new intelligent technology. Our group investigates computational principles that underlie natural intelligence and uses these principles to develop more capable and efficient intelligent machines. The department is responsible for the successful educational program in cognitive AI, which currently hosts about 600 AI students, and aims to develop the next generation of responsible AI systems. The department also operates the RobotLab which hosts several robots, e.g., humanoids, industrial arms and significant computational resources at its disposal for developing deep learning models and simulating neural networks, including GPU clusters.
Lanillos, P., Meo, C., Pezzato, C., Meera, A. A., Baioumy, M., Ohata, W., … & Tani, J. (2021). Active Inference in Robotics and Artificial Agents: Survey and Challenges. Under review IEEE T-RO [Paper]
Gumbsch, C., Butz, M. V., & Martius, G. (2021). Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains. Advances in Neural Information Processing Systems, 34. [Paper]