This paper demonstrates the effectiveness of learning based models for accurate, and reliable robot to human handoffs in various HRI scenarios. Specifically we bench marked a neuromorphic spiking neural network and a time series k-nearest neighbors classifier against traditional hand crafted force threshold methods. These models use linear force in the x, y, and z direction, as well as torque about the x, y, and z axis at the end effector of the robot arm to make handoff predictions. This paper demonstrates that these learning based methods are more robust to noise which occurs during operational use. We applied our algorithms to both stationary handoffs (stationary robot) and moving handoffs (robot walking). We believe that our evaluation is the first to examine walking handoffs. We evaluated all models in tests which determined the accuracy, precision, recall, f1, and average execution time for handoff events, noise events, and no event tests. We find that the SLAYER spiking neural network model performed the best across both walking and stationary handoffs for the majority of the evaluation criteria. Our results suggest that neuromorphic spiking neural networks are strong contenders for applications in time series, event based HRI applications.

Spiking Neural Networks for Improved Robot-Human Handoffs

In a constantly evolving robotics ecosystem, machine learning techniques have provided essential advancements in the field of Human-Robot Interaction. From reasoning to perception, traditional hand-crafted algorithms have been replaced by learning-based methods for improved performance. However, many of the state-of-the-art artificial neural networks and transformer models require large amounts of compute and power to run. While battery technology has improved, mobile robotic platforms are still restricted to low powered onboard computers in order to maximize their field operation time. These system requirements force roboticists to use algorithms that have high power requirements, reducing their running time or to settle for systems that are less power-hungry but also less accurate. Using robots on Navy ships makes this problem especially acute because power is at an especially high premium.

To address these power and computational limitations for mobile robots, researchers have looked towards the field of neuromorphic computing. Neuromorphic computing aims to create hardware and algorithms which use biologically inspired neural networks that are distinctly different from more traditional von Neumann computing. Spiking neural networks operate on event based, timeseries data making them excellent candidates for onboard robotic sensor data. Critically, spiking neural networks use far less power because each neuron only computes when it is needed (at an event).

In this report, our goal is to use robots for shipboard maintenance for Naval operations at sea. Robots should provide assistance with completing the dirty, dull, and dangerous work that Naval personnel carry out on a day-to-day basis. In order to accomplish this mission, these robots must be able to achieve state-of-the-art performance in reasoning, perception, and action while also operating on limited, battery-based power supplies. One of the common tasks that robots will be used for onboard will be finding, storing, and retrieving various tools for sailors to use. While mobile robots have excellent algorithms for picking up objects, current state of the art algorithms for giving a tool or object to a human sailor use a thresholding approach (e.g., release when a force is > ø). On any moving platform, a thresholding approach will simply not work – the robot will either drop the object if ø is too low or not let the object go if ø is too high. We designed a neuromorphic model that learned when a handoff occurred and then compared it to other learning-based approaches (e.g., K-Nearest Neighbors) and the traditional thresholding approach. These neuromorphic approaches outperform both approaches in both accuracy and speed and have a much lower power requirement. For future iterations of our work, we intend to test our neuromorphic models on neuromorphic hardware to comprehensively benchmark the power and performance improvements of our method.

Neuromorphic Robot-Human Handoffs

WeAT3 - Cooperation and Collaboration in Human-Robot Teams I

technical paper

[![](https://assets.underline.io/markdown_image/1/image/f25361a85b7c83a7476c0b3c1c0bb1b2.png)](https://drive.google.com/file/d/18jcAcciCBQHj6R07Dbpm2HCOBEIwAOCU/view?usp=sharing)


Dear Colleagues and Friends,

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IEEE RO-MAN 2024

The 33rd IEEE International Conference on Robot and Human Interactive Communication was held in Pasadena, California, USA. The theme of the IEEE RO-MAN 2024 was “Embracing Human-Centered HRI”.

Speaker: Nathaniel Gyory, Naval Research Laboratory (NRL)

Naval Applications II: Neuromorphic Robot-Human Handoffs

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NAML 2024

The 8th annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.


Greg Trafton

2

Presentations

Spiking Neural Networks for Improved Robot-Human Handoffs

Neuromorphic Robot-Human Handoffs

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