Socially Integrated Navigation with Reinforcement Learning Using Spiking Neural Networks Reduces Energy Consumption by 1.69 Orders of Magnitude
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Autonomous robots operating amongst people demand intelligent decision-making and efficient energy use, yet current navigation systems often struggle to replicate human-like awareness. Florian Tretter, Daniel Flögel, and Alexandru Vasilache, working at the FZI Research Center for Information Technology with colleagues including Max Grobbel and Jürgen Becker from the Karlsruhe Institute of Technology, now present a novel solution that bridges this gap. Their research introduces a new navigation system combining the strengths of both artificial and spiking neural networks, allowing robots to better understand and react to dynamic human environments.
The team demonstrates that this hybrid approach significantly improves a robot’s ability to navigate socially complex spaces, while also dramatically reducing its estimated energy consumption, a crucial step towards practical, everyday robotics.
Socially Aware Robot Navigation with Deep Learning This research details a new approach to robot navigation, focusing on creating robots that can safely and comfortably navigate crowded environments.
The team developed a system that combines deep reinforcement learning with spiking neural networks, a biologically realistic and potentially energy-efficient alternative to traditional artificial neural networks. The core challenge addressed is enabling robots to not only avoid collisions but also to respect personal space and anticipate human movements. The system learns through trial and error, maximizing a reward function designed to encourage safe and socially appropriate behavior. This reward function considers factors such as collision avoidance, respecting personal space, efficient movement towards a goal, and adaptation to human behavior. The robot perceives its surroundings by combining its own state, distance to the goal, velocity, and heading, with the states of nearby humans, including their position and velocity. By incorporating a history of human states, the robot can better predict their movements and react accordingly. The research demonstrates the feasibility of using spiking neural networks for complex tasks like robot navigation and introduces a carefully designed reward function that promotes socially acceptable behavior. This combination allows the robot to navigate crowded spaces safely and comfortably, representing a significant step towards more human-friendly robotic systems.
Hybrid Neural Networks for Efficient Robot Navigation Researchers have pioneered a new approach to autonomous robot navigation in crowded human environments, prioritizing both socially compliant behavior and energy efficiency. They developed a hybrid deep reinforcement learning architecture that strategically combines spiking neural networks with artificial neural networks. Spiking neural networks enable sparse, event-driven computation, while artificial neural networks provide training stability, facilitating an end-to-end neuromorphic pathway for policy inference and improving navigation performance. To accurately perceive and respond to dynamic surroundings, scientists designed a neuromorphic feature extractor capable of processing a variable number of surrounding agents. This feature extractor captures temporal crowd dynamics and human-robot interactions using event-based computation, allowing the robot to react to changes in the environment as they occur. The system defines both the robot and surrounding humans as agents, each influencing the overall navigation task.
Results demonstrate that this innovative approach achieves a substantial reduction in estimated energy consumption, approximately 1. 69 orders of magnitude, signifying a significant advancement in energy-efficient robotics and enabling robots to navigate crowded spaces more effectively while minimizing their environmental impact.
Neuromorphic Navigation Achieves Efficient Social Adaptation This research presents a novel approach to socially integrated navigation, introducing a hybrid deep reinforcement learning system that combines spiking neural networks and artificial neural networks. Researchers developed a system, termed SINRL, which utilizes a spiking feature extractor to efficiently encode observations of dynamic pedestrian environments and human-robot interactions. Experiments demonstrate that this approach outperforms existing methods in both social adaptation and navigational capability, achieving stable training through the incorporation of sigma-delta neurons. Notably, the team’s implementation on neuromorphic hardware resulted in estimated energy consumption reduced by approximately 1. 6 to 1. 7 orders of magnitude compared to conventional processors, highlighting the potential for substantial energy savings through the application of neuromorphic computing to robotics. Future research will focus on validating these energy benefits by deploying the trained spiking neural networks on the Loihi 2 processor, further exploring the capabilities of this innovative system in complex, real-world scenarios.
Hybrid Neural Networks Enable Efficient Robot Navigation Scientists developed a hybrid deep reinforcement learning framework that integrates spiking neural networks with artificial neural networks to enhance socially integrated navigation for autonomous robots.
The team’s approach utilizes a spiking neural network-based actor, responsible for robot actions, paired with an artificial neural network-based critic, which evaluates those actions, and a spiking feature extractor to capture complex human-robot interactions. Experiments demonstrate that this hybrid system significantly reduces estimated energy consumption, approximately 1. 69 orders of magnitude, compared to traditional artificial neural network-based approaches. The spiking feature extractor plays a crucial role by effectively capturing temporal crowd dynamics and learning an embedding of robot-human interactions, allowing the system to adapt to individual human preferences and adhere to social norms.
The team’s innovative use of the spiking feature extractor allows the system to handle a variable number of surrounding agents, stacking their observations in the temporal dimension of the spiking neural network to create a fixed-size input. Detailed energy analysis reveals substantial savings, with the system requiring significantly less energy per operation compared to conventional CPU and GPU-based systems. Measurements on devices like SpiNNaker and Loihi demonstrate the potential for ultra-low-power robotic navigation. 👉 More information 🗞 SINRL: Socially Integrated Navigation with Reinforcement Learning using Spiking Neural Networks 🧠 ArXiv: https://arxiv.org/abs/2512.07266 Tags: