neuROSym: Deployment and Evaluation of a ROS-based Neuro-Symbolic Model for Human Motion Prediction
- URL: http://arxiv.org/abs/2407.01593v1
- Date: Mon, 24 Jun 2024 11:13:06 GMT
- Title: neuROSym: Deployment and Evaluation of a ROS-based Neuro-Symbolic Model for Human Motion Prediction
- Authors: Sariah Mghames, Luca Castri, Marc Hanheide, Nicola Bellotto,
- Abstract summary: NeuroSyM has successfully embedded context with a qualitative Trajectory Calculus for spatial interactions representation.
We extend the original architecture to provide neuROSym, a ROS package for robot deployment in real-world scenarios.
We evaluated these models, NeuroSyM and a baseline SGAN, on a TIAGo robot in two scenarios with different human motion patterns.
- Score: 4.008958683836471
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Autonomous mobile robots can rely on several human motion detection and prediction systems for safe and efficient navigation in human environments, but the underline model architectures can have different impacts on the trustworthiness of the robot in the real world. Among existing solutions for context-aware human motion prediction, some approaches have shown the benefit of integrating symbolic knowledge with state-of-the-art neural networks. In particular, a recent neuro-symbolic architecture (NeuroSyM) has successfully embedded context with a Qualitative Trajectory Calculus (QTC) for spatial interactions representation. This work achieved better performance than neural-only baseline architectures on offline datasets. In this paper, we extend the original architecture to provide neuROSym, a ROS package for robot deployment in real-world scenarios, which can run, visualise, and evaluate previous neural-only and neuro-symbolic models for motion prediction online. We evaluated these models, NeuroSyM and a baseline SGAN, on a TIAGo robot in two scenarios with different human motion patterns. We assessed accuracy and runtime performance of the prediction models, showing a general improvement in case our neuro-symbolic architecture is used. We make the neuROSym package1 publicly available to the robotics community.
Related papers
- Fully Spiking Neural Network for Legged Robots [6.974746966671198]
Spiking Neural Network (SNN) is used to process legged robots, achieving outstanding results across a range of simulated terrains.
SNN holds a natural advantage over traditional neural networks in terms of inference speed and energy consumption.
Applying more biomimetic neural networks to legged robots can further reduce the heat dissipation and structural burden caused by the high power consumption of neural networks.
arXiv Detail & Related papers (2023-10-08T05:48:30Z) - CycleIK: Neuro-inspired Inverse Kinematics [12.29529468290859]
CycleIK is a neuro-robotic approach that wraps two novel neuro-inspired methods for the inverse kinematics (IK) task.
We show how embedding these into a hybrid neuro-genetic IK pipeline allows for further optimization.
arXiv Detail & Related papers (2023-07-21T13:03:27Z) - Robot Learning with Sensorimotor Pre-training [98.7755895548928]
We present a self-supervised sensorimotor pre-training approach for robotics.
Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens.
We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
arXiv Detail & Related papers (2023-06-16T17:58:10Z) - A Neuro-Symbolic Approach for Enhanced Human Motion Prediction [5.742409080817885]
We propose a neuro-symbolic approach for human motion prediction (NeuroSyM)
NeuroSyM weights differently the interactions in the neighbourhood by leveraging an intuitive technique for spatial representation called qualitative Trajectory Calculus (QTC)
Experimental results show that the NeuroSyM approach outperforms in most cases the baseline architectures in terms of prediction accuracy.
arXiv Detail & Related papers (2023-04-23T20:11:40Z) - Probabilistic Human Motion Prediction via A Bayesian Neural Network [71.16277790708529]
We propose a probabilistic model for human motion prediction in this paper.
Our model could generate several future motions when given an observed motion sequence.
We extensively validate our approach on a large scale benchmark dataset Human3.6m.
arXiv Detail & Related papers (2021-07-14T09:05:33Z) - Future Frame Prediction for Robot-assisted Surgery [57.18185972461453]
We propose a ternary prior guided variational autoencoder (TPG-VAE) model for future frame prediction in robotic surgical video sequences.
Besides content distribution, our model learns motion distribution, which is novel to handle the small movements of surgical tools.
arXiv Detail & Related papers (2021-03-18T15:12:06Z) - Few-Shot Visual Grounding for Natural Human-Robot Interaction [0.0]
We propose a software architecture that segments a target object from a crowded scene, indicated verbally by a human user.
At the core of our system, we employ a multi-modal deep neural network for visual grounding.
We evaluate the performance of the proposed model on real RGB-D data collected from public scene datasets.
arXiv Detail & Related papers (2021-03-17T15:24:02Z) - Cognitive architecture aided by working-memory for self-supervised
multi-modal humans recognition [54.749127627191655]
The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions.
Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task.
One solution is to make robots learn from their first-hand sensory data with self-supervision.
arXiv Detail & Related papers (2021-03-16T13:50:24Z) - Neuroevolution of a Recurrent Neural Network for Spatial and Working
Memory in a Simulated Robotic Environment [57.91534223695695]
We evolved weights in a biologically plausible recurrent neural network (RNN) using an evolutionary algorithm to replicate the behavior and neural activity observed in rats.
Our method demonstrates how the dynamic activity in evolved RNNs can capture interesting and complex cognitive behavior.
arXiv Detail & Related papers (2021-02-25T02:13:52Z) - A Semi-Supervised Assessor of Neural Architectures [157.76189339451565]
We employ an auto-encoder to discover meaningful representations of neural architectures.
A graph convolutional neural network is introduced to predict the performance of architectures.
arXiv Detail & Related papers (2020-05-14T09:02:33Z) - Hyperparameters optimization for Deep Learning based emotion prediction
for Human Robot Interaction [0.2549905572365809]
We have proposed an Inception module based Convolutional Neural Network Architecture.
The model is implemented in a humanoid robot, NAO in real time and robustness of the model is evaluated.
arXiv Detail & Related papers (2020-01-12T05:25:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.