5 papers · 1 filter
A real-time RGB-D perception pipeline for autonomous impact hammers in mining: self-filtering, rock segmentation and rock-breaking poses generation
MartÃn Gallegos, Francisco Leiva, Patricio Loncomilla +2
Impact hammers, also known as rock-breakers, are essential machines in mining operations, where they perform secondary reduction. In underground mining, these machines are typicall…
Let the Dynamics Flow: Stable Flow Matching Dynamical Systems
Rodrigo Pérez-Dattari, Francisco Leiva, Andrea Testa +3
Flow matching has recently emerged as a powerful approach for imitation learning, enabling scalable, expressive, and multimodal motion policies. However, when modeling these polici…
Data-driven control of hydraulic impact hammers under strict operational and control constraints
Francisco Leiva, Claudio Canales, Michelle Valenzuela +1
This paper presents a data-driven methodology for the control of static hydraulic impact hammers, also known as rock breakers, which are commonly used in the mining industry. The t…
Autonomous loading of ore piles with Load-Haul-Dump machines using Deep Reinforcement Learning
Rodrigo Salas, Francisco Leiva, Javier Ruiz-del-Solar
This work presents a deep reinforcement learning-based approach to train controllers for the autonomous loading of ore piles with a Load-Haul-Dump (LHD) machine. These controllers…
Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning
Francisco Leiva, Javier Ruiz-del-Solar
This paper proposes a method to combine reinforcement learning (RL) and imitation learning (IL) using a dynamic, performance-based modulation over learning signals. The proposed me…