arXiv subjects

Alejandro Romero

Publications and source records attributed to Alejandro Romero.

6 recordsLinked to original sources

arXiv2026

Educational robotics beyond ROS: a custom library for direct transfer of Webots controllers in the Turtlebot3

Robot Operating System (ROS) is becoming a standard in robotics courses at higher education, but its installation, configuration and distributed programming model can introduce a significant entry barrier in the new landscape of AI-based master and degree studies, where many students do not have a strong informatics background. This paper presents an educational experience that enables students to program a TurtleBot3 Burger directly in Python, while preserving a direct transfer path from simulation to the physical robot. The approach is based on a custom library that mirrors the Webots API, allowing the same controller code to be executed in the simulator and on the real TurtleBot3 with minimal modification. The library provides access to the robot motors, wheel encoders, inertial sensors and LiDAR, and is complemented by scripts that automate deployment and execution on the physical platform. It was used in a master-level course on introductory autonomous vehicles, and initial student feedback suggests that the approach reduces the perceived complexity of ROS-related infrastructure and helps learners focus on algorithmic logic and real-world behaviour.

cs.RO↗

arXiv2026

From Language to Drives: A Multimodal Framework for Aligning Autonomous Robots with Human Purposes

This work explores a multimodal approach to the alignment of the behavior of open-ended learning autonomous robots with human purposes through the use of large language models as cultural enablers. While autonomous robots can generate their own skills and goals through interaction with the environment, they often lack mechanisms to ensure that these remain relevant and aligned to those of human users. The proposed approach makes use of the general and cultural knowledge contained in LLMs and VLMs to establish a multimodal interactive alignment process in which human purposes, expressed in natural language, are transformed into motivational components of a robot architecture in the form of robot missions and drives that guide the behavior of the robot. The system is structured into three layers for semantic alignment, mission generation, and drive modeling. This design enables a bidirectional interaction loop: human inputs define high-level purposes, robots expand them into executable missions making use of multimodal information relayed by LLMs and VLMs, and feedback maintains alignment with human intent. Experiments in simulation and with a physical robot demonstrate that the process produces valid missions and behaviors, supporting effective human-agent alignment in interactive scenarios with autonomous robots.

cs.RO↗

arXiv2025

The Autonomy-Alignment Problem in Open-Ended Learning Robots: Formalising the Purpose Framework

The rapid advancement of artificial intelligence is enabling the development of increasingly autonomous robots capable of operating beyond engineered factory settings and into the unstructured environments of human life. This shift raises a critical autonomy-alignment problem: how to ensure that a robot's autonomous learning focuses on acquiring knowledge and behaviours that serve human practical objectives while remaining aligned with broader human values (e.g., safety and ethics). This problem remains largely underexplored and lacks a unifying conceptual and formal framework. Here, we address one of its most challenging instances of the problem: open-ended learning (OEL) robots, which autonomously acquire new knowledge and skills through interaction with the environment, guided by intrinsic motivations and self-generated goals. We propose a computational framework, introduced qualitatively and then formalised, to guide the design of OEL architectures that balance autonomy with human control. At its core is the novel concept of purpose, which specifies what humans (designers or users) want the robot to learn, do, or avoid, independently of specific task domains. The framework decomposes the autonomy-alignment problem into four tractable sub-problems: the alignment of robot purposes (hardwired or learnt) with human purposes; the arbitration between multiple purposes; the grounding of abstract purposes into domain-specific goals; and the acquisition of competence to achieve those goals. The framework supports formal definitions of alignment across multiple cases and proofs of necessary and sufficient conditions under which alignment holds. Illustrative hypothetical scenarios showcase the applicability of the framework for guiding the development of purpose-aligned autonomous robots.

cs.RO↗

arXiv2025

A Motivational Architecture for Open-Ended Learning Challenges in Robots

Developing agents capable of autonomously interacting with complex and dynamic environments, where task structures may change over time and prior knowledge cannot be relied upon, is a key prerequisite for deploying artificial systems in real-world settings. The open-ended learning framework identifies the core challenges for creating such agents, including the ability to autonomously generate new goals, acquire the necessary skills (or curricula of skills) to achieve them, and adapt to non-stationary environments. While many existing works tackles various aspects of these challenges in isolation, few propose integrated solutions that address them simultaneously. In this paper, we introduce H-GRAIL, a hierarchical architecture that, through the use of different typologies of intrinsic motivations and interconnected learning mechanisms, autonomously discovers new goals, learns the required skills for their achievement, generates skill sequences for tackling interdependent tasks, and adapts to non-stationary environments. We tested H-GRAIL in a real robotic scenario, demonstrating how the proposed solutions effectively address the various challenges of open-ended learning.

cs.RO↗

arXiv2025

Autonomous Generation of Sub-goals for Lifelong Learning in Robots

One of the challenges of open-ended learning in robots is the need to autonomously discover goals and learn skills to achieve them. However, when in lifelong learning settings, it is always desirable to generate sub-goals with their associated skills, without relying on explicit reward, as steppingstones to a goal. This allows sub-goals and skills to be reused to facilitate achieving other goals. This work proposes a two-pronged approach for sub-goal generation to address this challenge: a top-down approach, where sub-goals are hierarchically derived from general goals using intrinsic motivations to discover them, and a bottom-up approach, where sub-goal chains emerge from making latent relationships between goals and perceptual classes that were previously learned in different domains explicit. These methods help the robot to autonomously generate and chain sub-goals as a way to achieve more general goals. Additionally, they create more abstract representations of goals, helping to reduce sub-goal duplication and make the learning of skills more efficient. Implemented within an existing cognitive architecture for lifelong open-ended learning and tested with a real robot, our approach enhances the robot's ability to discover and achieve goals, generate sub-goals in an efficient manner, generalize learned skills, and operate in dynamic and unknown environments without explicit intermediate rewards.

cs.RO↗

arXiv2022

Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies

Autonomous open-ended learning is a relevant approach in machine learning and robotics, allowing the design of artificial agents able to acquire goals and motor skills without the necessity of user assigned tasks. A crucial issue for this approach is to develop strategies to ensure that agents can maximise their competence on as many tasks as possible in the shortest possible time. Intrinsic motivations have proven to generate a task-agnostic signal to properly allocate the training time amongst goals. While the majority of works in the field of intrinsically motivated open-ended learning focus on scenarios where goals are independent from each other, only few of them studied the autonomous acquisition of interdependent tasks, and even fewer tackled scenarios where goals involve non-stationary interdependencies. Building on previous works, we tackle these crucial issues at the level of decision making (i.e., building strategies to properly select between goals), and we propose a hierarchical architecture that treating sub-tasks selection as a Markov Decision Process is able to properly learn interdependent skills on the basis of intrinsically generated motivations. In particular, we first deepen the analysis of a previous system, showing the importance of incorporating information about the relationships between tasks at a higher level of the architecture (that of goal selection). Then we introduce H-GRAIL, a new system that extends the previous one by adding a new learning layer to store the autonomously acquired sequences of tasks to be able to modify them in case the interdependencies are non-stationary. All systems are tested in a real robotic scenario, with a Baxter robot performing multiple interdependent reaching tasks.

cs.LG↗