Francisco Munguia-Galeano

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• Doctor of Philosophy in Engineering
• Research Associate at University of Liverpool

In the context of self-driving laboratories (SDLs), ensuring automated and error-free capping is crucial, as it is a ubiquitous step in sample preparation. Automated capping in SDLs can occur in both large and small workspaces (e.g., inside a fume hood). However, most commercial capping machines are designed primarily for large spaces and are often...

Self-driving labs (SDLs) combine robotic automation with artificial intelligence (AI) to allow autonomous, high-throughput experimentation. However, robot manipulation in most SDL workflows operates in an open-loop manner, lacking real-time error detection and error correction. This can reduce reliability and overall efficiency. Here, we introduce...

Reinforcement Learning (RL) has shown outstanding capabilities in solving complex computational problems. However, most RL algorithms lack an explicit method for learning from contextual information. In reality, humans rely on context to identify patterns and relations among elements in the environment and determine how to avoid making incorrect ac...

Bagging is an essential skill that humans perform in their daily activities. However, deformable objects, such as bags, are complex for robots to manipulate. A learning‐based framework that enables robots to learn bagging is presented. The novelty of this framework is its ability to learn and perform bagging without relying on simulations. The lear...

Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid...

Cooperative human-robot interaction often requires successful handovers of objects between the two entities. However, the assumption that a human can reliably grasp an object from a robot is not always valid. To address this issue, we propose a vision-based tactile sensor for object handover framework that utilises a low-cost sensor with variable s...

Recent interest in additive manufacturing (AM) technologies (also known as 3D printing) has led to embedding multi-material and electronic components into 3D-printed structures. However, current 3D printing technologies fail to provide all the required materials to fabricate complex devices. Besides, the process of inserting individual building blo...

Planning precise manipulation in robotics to perform grasp and release-related operations, while interacting with humans is a challenging problem. Reinforcement learning (RL) has the potential to make robots attain this capability. In this paper, we propose an affordance-based human-robot interaction (HRI) framework, aiming to reduce the action spa...

This paper proposes Context-Sensitive Behaviors for Robots (CSBR), a method for generating diverse behaviors for robots in indoor environments based on five personality traits. This method is based on a novel model developed in this work that reacts to a synthetic genome that defines the personality of the robot. The model functions return differen...