Matias Mattamala University of Oxford, United Kingdom
Title of the talk: Vision-based Legged Robot Navigation for Field Applications
Abstract: Legged Robot Navigation for Field Applications" In this talk I'll discuss the challenges of deploying autonomous legged robots in the field. I'll present three main works that tackle perception, representation, and planning challenges when deploying quadruped robots for industrial inspection, underground exploration, and natural environments. I'll conclude the talk presenting a large-scale deployment of an autonomous legged robot for forest inventory applications, providing an outlook on open questions and future research avenues
Leandro Honorato de Souza Silva, Polytechnic School (University of Pernambuco) / Federal Institute of Education, Science and Technology of Paraíba (IFPB), Brazil
Title of the talk: Self-labeling Object Detection for Waste PCB Evaluation: Towards Sustainable Computer Vision Applications
Abstract: The increasing volume of electronic waste demands intelligent and scalable automation strategies. Printed Circuit Boards (PCBs), representing nearly 30% of all e-waste and containing a high concentration of valuable metals, are central to recycling efforts. However, the diversity of PCB structures and electronic components (ECs) poses challenges for automation. In this work, we propose a two-stage approach that bridges perception and action: a Self-labeling Electronic Component Detector and a robotic prototype for autonomous classification of PCBs. The first stage introduces the GEN Self-labeling Detector. This domain-adaptive, semi-supervised method uses iterative teacher-student training to detect ECs in images of waste PCBs. Tested with YOLOv5 and Faster R-CNN on the noisy FICS-PCB dataset, GEN achieves 67% mAP in the first generation, only 5% below fully supervised models. These detections are then used to characterize PCBs, estimating hazardous elements' presence and recycling's economic feasibility via the WPCB-EFA (Waste PCB Economic Feasibility Assessment) framework. The second stage translates perception into action by integrating this vision pipeline into a physical robotic system. The prototype consists of a conveyor-based classification machine, equipped with industrial automation components such as a PLC, HMI, and a servo-driven rack-and-pinion ejection system. All subsystems communicate through OPC protocols, ensuring closed-loop decision-making. This integrated solution advances self-supervised object detection in noisy, data-scarce domains. It is a model of how robotics and automation can drive sustainability in the circular economy.
Juan Pablo Vásconez Hurtado, Universidad Andrés Bello, Chile
Title of the talk: Human-Robot Interaction in Agriculture
Abstract: The agricultural industry has been significantly affected in recent years by labor shortages due to the migration of workers to urban areas. Additionally, the complexity of agricultural environments and tasks makes achieving high levels of autonomy challenging. Human-robot interaction (HRI) strategies can enhance efficiency and productivity in agricultural processes while reducing the workload of field workers, helping to mitigate the impact of labor shortages. These strategies aim to develop collaborative robotic systems that, through artificial intelligence (AI) algorithms, can interact more naturally, smoothly, and acceptably with humans during complex tasks such as harvesting, pruning, transport, and monitoring. HRI can play a key role in advancing sustainable and efficient agricultural technologies.
Rohit Singla, Universidad de O'Higgings, Chile,
Title of the talk: Robust Estimation in Teleoperated Robotics
Abstract: Time delays and parametric uncertainties remain key challenges in teleoperated robotics, affecting stability and performance. This work introduces a real-time estimation framework that addresses both. Time-varying delays are estimated by combining the Short-Time Fourier Transform with Taylor series expansion. Parametric uncertainties are handled via a block-processing approach integrated with Markov models and nonlinear filtering. The proposed framework is validated through simulations and experiments using a Geomagic Touch haptic interface. Results show improved tracking and estimation accuracy compared to standard methods. This approach enhances robustness and transparency in teleoperation, with relevance for remote surgery, space robotics, and hazardous environment applications.
Robert Guaman Rivera, Universidad de O'Higgings, Chile
Title of the talk: Automated Construction: Trajectory Control, Obstacle Avoidance and Automated Monitoring in 3D Printing Applications
Abstract: The workspace of a robotic platform on construction sites plays a crucial role in ensuring the progress of the work, maintaining task safety, preventing damage to the robot, and ensuring the quality of the printed profile. This talk will present the study of a trajectory tracking controller with obstacle avoidance, emphasising the importance of minimising printing errors to guarantee optimal structural quality. Despite the advancements in robotics and 3D printing, one of the main challenges remains improving quality monitoring techniques during and after printing. In this context, the study will explore using deep neural networks and computer vision to automatically detect objects and obstacles in the working environment, aiming to scale this approach and monitor the quality of the printed profile on construction sites more efficiently.
Felipe Inostroza, AMTC, Chile
Title of the talk: Use of Detection Statistics in Map Building and Localization (SLAM)
Abstract: Simultaneous Localization and Mapping (SLAM) is considered one of the fundamental problems in robotics. The most popular SLAM solutions rely on heuristic procedures, with varying levels of rigor, to handle detection uncertainty and data association. To incorporate this part of the problem into the Bayesian estimation, the theory of Random Finite Sets (RFS) can be used. RFSs allow modeling the map and its observations as sets, naturally incorporating detection uncertainty through a detection probability and a false alarm distribution. This presentation explains the Joint-Vector-Set SLAM algorithm, which uses RFSs to solve the SLAM problem through a graph-based optimization strategy. Competitive results with the state of the art are shown, using these detection statistics in the SLAM process, which reduces the number of heuristics required to solve the problem, at the cost of increased computational load.
