# Intelligent machines work in unstructured environments by differential neuromorphic computing

arXiv: https://arxiv.org/abs/2309.08835 · HTML: https://mlsanity.com/paper/2309.08835/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 87 references that are in the map.
- First posted on arXiv 16 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification](https://mlsanity.com/paper/2010.05300/) (2020)
- [Computing Systems for Autonomous Driving: State-of-the-Art and Challenges](https://mlsanity.com/paper/2009.14349/) (2020)
- [The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity](https://mlsanity.com/paper/2201.11063/) (2022)
- [Robotic Tactile Perception of Object Properties: A Review](https://mlsanity.com/paper/1711.03810/) (2017)
- [Real-Time Context-aware Detection of Unsafe Events in Robot-Assisted Surgery](https://mlsanity.com/paper/2005.03611/) (2020)
- [Neuromorphic Event-Based Slip Detection and suppression in Robotic Grasping and Manipulation](https://mlsanity.com/paper/2004.07386/) (2020)

## Built on by (most cited first, 1 of 1)

- [Neuromorphic spatiotemporal optical flow: Enabling ultrafast visual perception beyond human capabilities](https://mlsanity.com/paper/2409.15345/) (2024)

## Abstract (arXiv)

Efficient operation of intelligent machines in the real world requires methods that allow them to understand and predict the uncertainties presented by the unstructured environments with good accuracy, scalability and generalization, similar to humans. Current methods rely on pretrained networks instead of continuously learning from the dynamic signal properties of working environments and suffer inherent limitations, such as data-hungry procedures, and limited generalization capabilities. Herein, we present a memristor-based differential neuromorphic computing, perceptual signal processing and learning method for intelligent machines. The main features of environmental information such as amplification (>720%) and adaptation (<50%) of mechanical stimuli encoded in memristors, are extracted to obtain human-like processing in unstructured environments. The developed method takes advantage of the intrinsic multi-state property of memristors and exhibits good scalability and generalization, as confirmed by validation in two different application scenarios: object grasping and autonomous driving. In the former, a robot hand experimentally realizes safe and stable grasping through fast learning (in ~1 ms) the unknown object features (e.g., sharp corner and smooth surface) with a single memristor. In the latter, the decision-making information of 10 unstructured environments in autonomous driving (e.g., overtaking cars, pedestrians) is accurately (94%) extracted with a 40*25 memristor array. By mimicking the intrinsic nature of human low-level perception mechanisms, the electronic memristive neuromorphic circuit-based method, presented here shows the potential for adapting to diverse sensing technologies and helping intelligent machines generate smart high-level decisions in the real world.