Learning search behaviour from humans

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Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

Multi-Object Tracking (MOT) is a fundamental computer vision task that involves detecting objects of interest in video frames and associating detections of the same object across time to form trajectories. For association, appearance cues, extracted throug ...

Model-based reinforcement learning for robot control offers the advantages of overcoming concerns on data collection and iterative processes for policy improvement in model-free methods. However, both methods use exploration strategy relying on heuristics ...

We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a safe set while reaching a target set within a finite time horizon. Due to the t ...

In digital timber construction, scanning technologies and point cloud data are widely used due to the accessibility of affordable 3D sensors, photogrammetry, and userfriendly CAD tools. While typically not employed for accuracy checks in timber fabrication ...

Soft robots aim to revolutionize how robotic systems interact with the environment thanks to their inherent compliance. Some of these systems are even able to modulate their physical softness. However, simply equipping a robot with softness will not genera ...

Web search engines process billions of queries daily, making the balance between computational efficiency and ranking quality crucial. While neural ranking models have shown impressive performance, their computational costs, particularly in feature extract ...

Order, regularities, and patterns are ubiquitous around us. A flock of birds maneuvering in the sky, the self-organization of social insects, a global pandemic or a traffic jam are examples of complex systems where the macroscopic patterns arise from the m ...

In this thesis, we address the complex issue of collision avoidance in the joint space of robots. Avoiding collisions with both the robot's own body parts and obstacles in the environment is a critical constraint in motion planning and is crucial for ensur ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting ...

During reaching actions, the human central nerve system (CNS) generates the trajectories that optimize effort and time. When there is an obstacle in the path, we make sure that our arm passes the obstacle with a sufficient margin. This comfort margin varie ...

Semi-supervised semantic segmentation methods leverage unlabeled data by pseudo-labeling them. Thus the success of these methods hinges on the reliability of the pseudo-labels. Existing methods mostly choose high-confidence pixels in an effort to avoid err ...

There is a growing trend towards designing learning activities featuring robots as collaborative exercises where children work together to achieve the activity objectives, generating interactions that can trigger learning processes. Witnessing such activit ...

The collective behavior of animals has been traditionally studied through observation, quantitative models of behavior, and devices of low intelligence. Nowadays, the advancements in the field of robotics allow for closed-loop experiments that occur in rea ...

We address multi-robot safe mission planning in uncertain dynamic environments. This problem arises in several applications including safety-critical exploration, surveillance, and emergency rescue missions. Computation of a multi-robot optimal control pol ...

Programming intelligent robots requires robust controllers that can achieve desired tasks while adapting to the changes in the task and the environment. In this thesis, we address the challenges in designing such adaptive and anticipatory feedback controll ...

Fully exploiting soft robots' capabilities requires devising strategies that can accurately control their movements with the limited amount of control sources available. This task is challenging for reasons including the hard-to-model dynamics, the system' ...

Learning search behaviour from humans

Chattez avec Graph Search

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Predicting the long-term collective behaviour of fish pairs with deep learning

Person Re-Identification and its Application to Multi-Object Tracking

Exploration-based model learning with self-attention for risk-sensitive robot control

A learning-based approach to stochastic optimal control under reach-avoid constraint

DiffCheck: A scan-CAD evaluation tool for digital manufacturing and assembly processes in timber construction

Prescribing Cartesian Stiffness of Soft Robots by Co-Optimization of Shape and Segment-Level Stiffness

Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search

Human-Robot Swarm Interaction: An Explorative Path to Foster Complex Systems Understanding

Implicit Distance Functions: Learning and Applications in Robotics

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

SaliencyMix plus : Noise-Minimized Image Mixing Method With Saliency Map in Data Augmentation

Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation

Computational Models of Mutual Understanding for Human-Robot Collaborative Learning

A biohybrid interaction framework for the integration of robots in animal societies

Multi-robot task allocation for safe planning against stochastic hazard dynamics

Learning and optimization of anticipatory feedback controllers for robot manipulation

A Provably Stable Iterative Learning Controller for Continuum Soft Robots