Search Results for author: Sariah Mghames

Found 11 papers, 3 papers with code

neuROSym: Deployment and Evaluation of a ROS-based Neuro-Symbolic Model for Human Motion Prediction

no code implementations24 Jun 2024 Sariah Mghames, Luca Castri, Marc Hanheide, Nicola Bellotto

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.

Human motion prediction Motion Detection +1

Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios

no code implementations7 Jun 2024 Luca Castri, Gloria Beraldo, Sariah Mghames, Marc Hanheide, Nicola Bellotto

To bridge this gap, in our previous work we proposed ROS-Causal, a ROS-based framework designed for onboard data collection and causal discovery in human-robot spatial interactions.

Causal Discovery Causal Inference

Learning Manipulation Tasks in Dynamic and Shared 3D Spaces

1 code implementation26 Apr 2024 Hariharan Arunachalam, Marc Hanheide, Sariah Mghames

Automating the segregation process is a need for every sector experiencing a high volume of materials handling, repetitive and exhaustive operations, in addition to risky exposures.

Efficient Causal Discovery for Robotics Applications

no code implementations23 Oct 2023 Luca Castri, Sariah Mghames, Nicola Bellotto

Using robots for automating tasks in environments shared with humans, such as warehouses, shopping centres, or hospitals, requires these robots to comprehend the fundamental physical interactions among nearby agents and objects.

Causal Discovery

Qualitative Prediction of Multi-Agent Spatial Interactions

no code implementations30 Jun 2023 Sariah Mghames, Luca Castri, Marc Hanheide, Nicola Bellotto

The third approach implements a purely data-driven network for motion prediction, the output of which is post-processed to predict QTC spatial interactions.

motion prediction

A Neuro-Symbolic Approach for Enhanced Human Motion Prediction

1 code implementation23 Apr 2023 Sariah Mghames, Luca Castri, Marc Hanheide, Nicola Bellotto

Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e. g. robots).

Human motion prediction motion prediction +2

Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions

no code implementations29 Oct 2022 Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto

Exploiting robots for activities in human-shared environments, whether warehouses, shopping centres or hospitals, calls for such robots to understand the underlying physical interactions between nearby agents and objects.

Causal Discovery

Environment-aware Interactive Movement Primitives for Object Reaching in Clutter

no code implementations28 Oct 2022 Sariah Mghames, Marc Hanheide

The majority of motion planning strategies developed over the literature for reaching an object in clutter are applied to two dimensional (2-d) space where the state space of the environment is constrained in one direction.

Motion Planning

Interactive Movement Primitives: Planning to Push Occluding Pieces for Fruit Picking

no code implementations27 Apr 2020 Sariah Mghames, Marc Hanheide, Amir Ghalamzan E

Nonetheless, existing approaches to planning pushing movements in cluttered environments either are computationally expensive or only deal with 2-D cases and are not suitable for fruit picking, where it needs to compute 3-D pushing movements in a short time.

Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments

no code implementations26 Feb 2020 Manuel Fernandez-Carmona, Sariah Mghames, Nicola Bellotto

This paper, therefore, presents a new approach for temporal modelling of long-term human activities with smart-home sensors, which is used to detect anomalous situations in a robot-assisted environment.

Anomaly Detection

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