Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack

TL;DR

The paper addresses enabling embodied intelligence in robotics through neuromorphic AI based on Spiking Neural Networks (SNNs) to overcome limitations of traditional robotic computing in dynamic environments while aiming for low energy, reliability, and security. It proposes a holistic development stack with cross-layer HW/SW optimizations, representative benchmarks, fault mitigation, and security measures, aiming to close the gap between perception, action, and continual learning. The authors outline six perspectives (P1–P6) guiding research on learning quality, energy efficiency, benchmarks, reliability, security, and integrated toolchains, and discuss current trends and open challenges with preliminary evidence. The work emphasizes end-to-end, energy-aware, and robust neuromorphic robotics as essential for practical deployment in real-world settings and provides a roadmap for future research and development.

Abstract

Robotic technologies have been an indispensable part for improving human productivity since they have been helping humans in completing diverse, complex, and intensive tasks in a fast yet accurate and efficient way. Therefore, robotic technologies have been deployed in a wide range of applications, ranging from personal to industrial use-cases. However, current robotic technologies and their computing paradigm still lack embodied intelligence to efficiently interact with operational environments, respond with correct/expected actions, and adapt to changes in the environments. Toward this, recent advances in neuromorphic computing with Spiking Neural Networks (SNN) have demonstrated the potential to enable the embodied intelligence for robotics through bio-plausible computing paradigm that mimics how the biological brain works, known as "neuromorphic artificial intelligence (AI)". However, the field of neuromorphic AI-based robotics is still at an early stage, therefore its development and deployment for solving real-world problems expose new challenges in different design aspects, such as accuracy, adaptability, efficiency, reliability, and security. To address these challenges, this paper will discuss how we can enable embodied neuromorphic AI for robotic systems through our perspectives: (P1) Embodied intelligence based on effective learning rule, training mechanism, and adaptability; (P2) Cross-layer optimizations for energy-efficient neuromorphic computing; (P3) Representative and fair benchmarks; (P4) Low-cost reliability and safety enhancements; (P5) Security and privacy for neuromorphic computing; and (P6) A synergistic development for energy-efficient and robust neuromorphic-based robotics. Furthermore, this paper identifies research challenges and opportunities, as well as elaborates our vision for future research development toward embodied neuromorphic AI for robotics.

Figures (9)

• Figure 1: An overview of the embodied neuromorphic AI for robotics, which encompasses performance aspect like accuracy, adaptability, and efficiency, while considering reliability and security aspects.
• Figure 2: The system overview of the neuromorphic AI-based robotic system, showing the full robotics' processing pipeline.
• Figure 3: Overview of an SNN processing pipeline, showing different SNN components of such as the network architecture with neurons and synapses as well as neural coding (i.e., encoding and decoding).
• Figure 4: SNN accuracy in the unsupervised continual learning settings on the MNIST dataset, across different precision levels and methods: Baseline Ref_Diehl_STDPmnist_FNCOM15, SpikeDyn Ref_Putra_SpikeDyn_DAC21, and lpSpikeCon Ref_Putra_lpSpikeCon_IJCNN22; adapted from studies in Ref_Putra_lpSpikeCon_IJCNN22. Here, samples from each task (class) are fed to the SNN and learned sequentially to simulate dynamic environments, then the SNN is expected to learn new tasks without facing catastrophic forgetting, i.e., forgetting the previously learned knowledge (tasks) after learning new information.
• Figure 5: (a) Neuron elimination and quantization in the FSpiNN Ref_Putra_FSpiNN_TCAD20 improve energy efficiency from the SL-STDP Ref_Srinivasan_SLSTDP_IJCNN17 and the baseline model Ref_Diehl_STDPmnist_FNCOM15; adapted from studies in Ref_Putra_FSpiNN_TCAD20. (b) Accuracy and memory footprints of different SNNs for the CIFAR10 dataset: ResNet11, ResNet19, CIFARNet1, CIFARNet2, AutoSNN, and SpikeNAS; adapted from studies in Ref_Putra_SpikeNAS_arXiv24.