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Found 13 papers, 3 papers with code
Robot Perception of Static and Dynamic Objects with an Autonomous Floor Scrubber
The proposed system is based on multiple sensors including 3D and 2D lidar, two RGB-D cameras and a stereo camera.
Robotics
ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications
Deploying robots in human-shared spaces requires understanding interactions among nearby agents and objects.
A Neuro-Symbolic Approach for Enhanced Human Motion Prediction
Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e. g. robots).
A Directionally Selective Neural Network with Separated ON and OFF Pathways for Translational Motion Perception in a Visually Cluttered Environment
With respect to biological findings underlying fly's physiology in the past decade, we present a directionally selective neural network, with a feed-forward structure and entirely low-level visual processing, so as to implement direction selective neurons in the fly's visual system, which are mainly sensitive to wide-field translational movements in four cardinal directions.
A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios
This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios.
Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking
Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets.
Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments
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.
Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior
Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets.
Neural Task Success Classifiers for Robotic Manipulation from Few Real Demonstrations
We compared the performances of our novel classifier and the existing models using our dataset and the MIME dataset.
Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions
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.
Towards Long-term Autonomy: A Perspective from Robot Learning
In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention.
Qualitative Prediction of Multi-Agent Spatial Interactions
The third approach implements a purely data-driven network for motion prediction, the output of which is post-processed to predict QTC spatial interactions.
Efficient Causal Discovery for Robotics Applications
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.
