# Nengo and low-power AI hardware for robust, embedded neurorobotics

arXiv: https://arxiv.org/abs/2007.10227 · HTML: https://mlsanity.com/paper/2007.10227/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 15 of 15)

- [Event-driven Vision and Control for UAVs on a Neuromorphic Chip](https://mlsanity.com/paper/2108.03694/) (2021)
- [The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/2106.07030/) (2021)
- [The whole brain architecture approach: Accelerating the development of artificial general intelligence by referring to the brain](https://mlsanity.com/paper/2103.06123/) (2021)
- [Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware](https://mlsanity.com/paper/2408.03336/) (2024)
- [Design optimization for high-performance computing using FPGA](https://mlsanity.com/paper/2304.12474/) (2023)
- [Exploiting FPGA Capabilities for Accelerated Biomedical Computing](https://mlsanity.com/paper/2307.07914/) (2023)
- [Demonstrating Analog Inference on the BrainScaleS-2 Mobile System](https://mlsanity.com/paper/2103.15960/) (2021)
- [Learning over time using a neuromorphic adaptive control algorithm for robotic arms](https://mlsanity.com/paper/2210.01243/) (2022)
- [Investigating Continuous Learning in Spiking Neural Networks](https://mlsanity.com/paper/2310.05343/) (2023)
- [Harnessing FPGA Technology for Enhanced Biomedical Computation](https://mlsanity.com/paper/2311.12439/) (2023)
- [Probabilistic Modeling of Spiking Neural Networks with Contract-Based Verification](https://mlsanity.com/paper/2506.13340/) (2025)
- [Bellman Memory Units: A neuromorphic framework for synaptic reinforcement learning with an evolving network topology](https://mlsanity.com/paper/2511.16066/) (2025)
- [Governance at the Edge of Architecture: Regulating NeuroAI and Neuromorphic Systems](https://mlsanity.com/paper/2602.01503/) (2026)
- [S2Act: Simple Spiking Actor](https://mlsanity.com/paper/2603.15725/) (2026)
- [SNN-MLIR: An MLIR Dialect for Compiling Neuromorphic SNNs from NIR to Bare-Metal C](https://mlsanity.com/paper/2606.09213/) (2026)

## Abstract (arXiv)

In this paper we demonstrate how the Nengo neural modeling and simulation libraries enable users to quickly develop robotic perception and action neural networks for simulation on neuromorphic hardware using familiar tools, such as Keras and Python. We identify four primary challenges in building robust, embedded neurorobotic systems: 1) developing infrastructure for interfacing with the environment and sensors; 2) processing task specific sensory signals; 3) generating robust, explainable control signals; and 4) compiling neural networks to run on target hardware. Nengo helps to address these challenges by: 1) providing the NengoInterfaces library, which defines a simple but powerful API for users to interact with simulations and hardware; 2) providing the NengoDL library, which lets users use the Keras and TensorFlow API to develop Nengo models; 3) implementing the Neural Engineering Framework, which provides white-box methods for implementing known functions and circuits; and 4) providing multiple backend libraries, such as NengoLoihi, that enable users to compile the same model to different hardware. We present two examples using Nengo to develop neural networks that run on CPUs, GPUs, and Intel's neuromorphic chip, Loihi, to demonstrate this workflow. The first example is an end-to-end spiking neural network that controls a rover simulated in Mujoco. The network integrates a deep convolutional network that processes visual input from mounted cameras to track a target, and a control system implementing steering and drive functions to guide the rover to the target. The second example augments a force-based operational space controller with neural adaptive control to improve performance during a reaching task using a real-world Kinova Jaco2 robotic arm. Code and details are provided with the intent of enabling other researchers to build their own neurorobotic systems.