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Found 12 papers, 6 papers with code
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Fully neuromorphic vision and control for autonomous drone flight
Robotic experiments show a successful sim-to-real transfer of the fully learned neuromorphic pipeline.
Evolved neuromorphic radar-based altitude controller for an autonomous open-source blimp
Robotic airships offer significant advantages in terms of safety, mobility, and extended flight times.
The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition
Robotics is the next frontier in the progress of Artificial Intelligence (AI), as the real world in which robots operate represents an enormous, complex, continuous state space with inherent real-time requirements.
Design and implementation of a parsimonious neuromorphic PID for onboard altitude control for MAVs using neuromorphic processors
In this paper, we propose a parsimonious and adjustable neuromorphic PID controller, endowed with a minimal number of 93 neurons sparsely connected to achieve autonomous, onboard altitude control of a quadrotor equipped with Intel's Loihi neuromorphic chip.
Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes
Our experiments show that depth estimation is substantially improved on low-texture scenes, without any loss on textured scenes, when compared to Monodepth by Godard et al. Secondly, we show that training with an application's representative rotations, in both pitch and roll, is sufficient to significantly improve performance over the entire range of expected rotation.
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks
We focus on the complex task of learning to estimate optical flow from event-based camera inputs in a self-supervised manner, and modify the state-of-the-art ANN training pipeline to encode minimal temporal information in its inputs.
A toolbox for neuromorphic sensing in robotics
This initiative is meant to stimulate and facilitate robotic integration of neuromorphic AI, with the opportunity to adapt traditional off-the-shelf sensors to spiking neural nets within one of the most powerful robotic tools, ROS.
FAITH: Fast iterative half-plane focus of expansion estimation using event-based optic flow
This is achieved by means of an event-based camera, along with a fast RANSAC-based algorithm that uses event-based OF to determine the FOE.
Neuromorphic control for optic-flow-based landings of MAVs using the Loihi processor
However, a major challenge for using such processors on robotic platforms is the reality gap between simulation and the real world.
Fusion of stereo and still monocular depth estimates in a self-supervised learning context
We study how autonomous robots can learn by themselves to improve their depth estimation capability.
Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance
We study this persistent form of SSL in the context of a flying robot that has to avoid obstacles based on distance estimates from the visual cue of stereo vision.
Robotics
