In conjunction with RSS 2026
Monday, July 13th
Sydney, Australia
Drawing inspiration from biological neural systems, neuromorphic sensing and perception techniques fundamentally change the way in which robots perceive their environment. The event-driven, sparse nature of neuromorphic sensing and perception provides significant advantages in terms of energy and latency. This enables the creation of high-endurance, agile robots that can operate autonomously in unconstrained, dynamic environments with limited computational resources. Such sensing and computational systems showcase significant potential to form the core of fully autonomous future robots. The asynchronous, low-latency nature of neuromorphic sensor data enables high-fidelity navigation and control. At the same time, its large dynamic range enhances perception robustness, especially in poor or rapidly changing lighting. In planning and decision-making, the sparse and efficient data representation of neuromorphic sensors allows control policies to operate at lower computational cost, supporting online learning and adaptation as robots encounter new scenarios. The inherent compatibility of neuromorphic systems with biologically inspired models further enhances the bio-plausibility of such systems in task-level reasoning and long-horizon planning under uncertainty.
This workshop focuses on the integration of neuromorphic sensing, navigation, planning, and control pipelines in real-world robotics. Going beyond purely simulation-based studies, we emphasize demonstrations where neuromorphic hardware and software are integrated directly into a robot. Such an integration improves closed-loop autonomy in scenarios ranging from agile locomotion under power constraints to high-speed manipulation in dynamic settings. We invite contributions addressing comparisons between – neuromorphic and conventional methods; hybrid systems (where classical perception techniques work in tandem with neuromorphic sensing and perception); and novel algorithms that leverage asynchronous data for planning, prediction, and robust control under uncertainty. In the workshop, we will explore emerging challenges and experimental advances needed to drive neuromorphic navigation and control technologies into next-generation robotic platforms.