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Found 15 papers, 4 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.
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).
LTS-NET: End-to-end Unsupervised Learning of Long-Term 3D Stable objects
In this research, we present an end-to-end data-driven pipeline for determining the long-term stability status of objects within a given environment, specifically distinguishing between static and dynamic objects.
Collection and Evaluation of a Long-Term 4D Agri-Robotic Dataset
Long-term autonomy is one of the most demanded capabilities looked into a robot.
Premonition Net, A Multi-Timeline Transformer Network Architecture Towards Strawberry Tabletop Yield Forecasting
Yield forecasting is a critical first step necessary for yield optimisation, with important consequences for the broader food supply chain, procurement, price-negotiation, logistics, and supply.
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.
Environment-aware Interactive Movement Primitives for Object Reaching in Clutter
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.
Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions
The robot behaviour planning is embedded in a Reinforcement Learning (RL) framework, where the objective is maximising the level of overall user engagement during the interactions.
EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting
We present automatically parameterised Fully Homomorphic Encryption (FHE) for encrypted neural network inference and exemplify our inference over FHE compatible neural networks with our own open-source framework and reproducible examples.
Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning
We adopt a recent probabilistic recurrent neural network architecture, called Re-current Kalman Networks (RKNs), to model learning by conditioning its transition dynamics on the control actions.
Interactive Movement Primitives: Planning to Push Occluding Pieces for Fruit Picking
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.
Are you still with me? Continuous Engagement Assessment from a Robot's Point of View
Continuously measuring the engagement of users with a robot in a Human-Robot Interaction (HRI) setting paves the way towards in-situ reinforcement learning, improve metrics of interaction quality, and can guide interaction design and behaviour optimisation.
Robotics
Artificial Intelligence for Long-Term Robot Autonomy: A Survey
Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics.
3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data
Our approach, T-Pose-LSTM (Temporal 3DOF-Pose Long-Short-Term Memory), is trained using long-term data from real-world robot deployments and aims to learn context-dependent (environment- and time-specific) human activities.
