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Found 5 papers, 2 papers with code
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.
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.
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.
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).
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.
