Neuromorphic Computing
Energy-efficient AI for mobile and industrial applications
Neuromorphic Computing
Our research activities center on improving the learning capability and intelligence of technical systems, whether in the manufacturing, automobile and robotics. To carry out these activities, we rely on findings from the field of neurobiology and apply software methods from the area of artificial intelligence (AI) and the subarea of deep learning. We also develop algorithms and software for highly energy-efficient neuromorphic hardware and its use in the area of machine learning.
Research focus
- Neuromorphic robotics
Controlling mobile or industrial robotics in an energy efficient way with low latency is critical for tomorrow's autonomous robots. More AI will have to be processed on device, as robots and objects get more intelligent and it will have to save on battery life. Neuromorphically controlled robotics is an essential topic with problems such as SLAM, motion control, online learning, … In this research line, we focus on motion control, with two projects, one about robot swimming (INRC1), one about object insertion with a robotic arm using reinforcement learning (INRC3). On the simulation side, in the frame of the Human Brain Project, we participate in the development of the Neurorobotics Platform that is a central tool for all our projects.
- Neuromorphic vision
Event-based sensing, and in particular vision, is essential to robotics. So this second research line actually serves the first one, though it is more recent. In this research line, that could be applied in automotive, security, military, smartphone, medicals, household electronics, etc, we focus on mobile robotics (drones in the FAMOUS project), industrial robotics (robotic arm in ELEANOR) and human-machine interaction in an upcoming project. We also explore opportunities in space applications and automotive.
Open-Source-Tool
Neurorobotics Platform
3D simulator for neural network controlled robots
Development of intelligent robotic systems through the simulation of virtual 3D robots and neural networks in a shared environment.
Tutorials
These tutorials are aimed at engineers and R&D managers in large and small industrial companies. They present a revolutionary technology for on-board artificial intelligence (edge-AI) in terms of energy efficiency and latency: neuromorphic computing. Gains of several orders of magnitude are possible!
Moreover, certain neuromorphic chips enable on-chip learning, and therefore on-line learning and system adaptability. For example, a system that has been pre-trained for some people can be adapted for others in just a few seconds online. In this way, systems can self-improve. We are convinced that neuromorphic computing offers solutions for many embedded AI applications in automotive, aerospace and medical technology, robotics, logistics, consumer electronics and, of course, smartphones.
Offer for students
Munich neuromorphic hackathon
fortiss - TUM Klinikum: Neuromorphic Brain-Computer Interface
Real-world industrial applications of neuromorphic computing
Contact
fortiss Lab
Projects
Publications
- SpikeClouds: Streaming Spike-Based Processing of LiDAR for Fast and Efficient Object Detection Robotics and Automation Letters, 10(8):8411-8418, 2025. Details URL DOI BIB
- EEvAct: Early Event-Based Action Recognition with High-Rate Two-Stream Spiking Neural Networks. pages 41-48, 2025. IEEE Press. Details URL DOI BIB
- TONUS: Neuromorphic human pose estimation for artistic sound co-creation pages 1-8, 2025. IEEE. Details URL DOI BIB
- Neuromorphic force-control in an industrial task: validating energy and latency benefits In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 717-724, Abu Dhabi, UAE, 2024. Details URL DOI BIB
- Scaling Up Resonate-and-Fire Networks for Fast Deep Learning In Computer Vision - ECCV 2024, volume 15059 of Lecture Notes in Computer Science, 2024. Details URL DOI BIB
- Dynamic Event-based Optical Identification and Communication Frontiers in Neurorobotics, 18():, 2024. Details URL DOI BIB
- Neurorobotic reinforcement learning for domains with parametrical uncertainty Frontiers in Neurorobotics, 17():, 2023. Details URL DOI BIB
- Generating Event-Based Datasets for Robotic Applications Using MuJoCo-ESIM In Proceedings of the 2023 International Conference on Neuromorphic Systems, volume 1 of ICONS 23, pages 7, New York, NY, USA, 2023. Association for Computing Machinery, Association for Computing Machinery. Details URL DOI BIB
- Neuromorphic Optical Flow and Real-time Implementation with Event Cameras IBM Research Zürich, IEEE Conference on Computer Vision and Pattern Recognition Workshops 2023. Details URL DOI BIB
- Spiking Neural Units ermöglichen effiziente ereignisgesteuerte Kameras blog, 2022. Details URL BIB
- A Spiking Central Pattern Generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards Neuromorphic Computing and Engineering, 1(1):, 2021. Details URL DOI BIB
- Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience Frontiers in Systems Neuroscience, 14():, 2020. Details URL DOI BIB
- The Neurorobotics Platform for Teaching – Embodiment Experiments with Spiking Neural Networks and Virtual Robots In 2019 IEEE International Conference on Cyborg and Bionic Systems (CBS), 2019. IEEE. Details URL DOI BIB
- A Biomimetic Control Method Increases the Adaptability of a Humanoid Robot Acting in a Dynamic Environment Frontiers in Neurorobotics, 13():, 2019. Details URL PDF DOI BIB
- Running Large-Scale Simulations on the Neurorobotics Platform to Understand Vision – The Case of Visual Crowding Frontiers in Neurorobotics, 13():, 2019. Details URL PDF DOI BIB
- Body Randomization Reduces the Sim-to-Real Gap for Compliant Quadruped Locomotion Frontiers in Neurorobotics, 13():, 2019. Details URL PDF DOI BIB
- The Collaborative Virtual Reality Neurorobotics Lab In Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pages 1671–1674, Osaka, Japan, 2019. IEEE. Details URL PDF DOI BIB
- Roboter mit Hirn blog, 2018. Details URL BIB
- Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform Frontiers in Neurorobotics, 11():, 2017. Details URL PDF DOI BIB
- A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulation In Proceedings of the IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages 1179–1184, Seoul, South Korea, 2015. Details URL PDF DOI BIB