Author Archives: Juan-antonio Fernández-madrigal

It seems that the human brain working memory uses pointers

March 6, 2025 10:21 ,

Edward Awh, Edward K. Vogel, Working memory needs pointers, Trends in Cognitive Sciences, Volume 29, Issue 3, 2025, Pages 230-241, DOI: 10.1016/j.tics.2024.12.006.

Cognitive neuroscience has converged on a definition of working memory (WM) as a capacity-limited system that maintains highly accessible representations via stimulus-specific neural patterns. We argue that this standard definition may be incomplete. We highlight the fundamental need to recognize specific instances or tokens and to bind those tokens to the surrounding context. We propose that contextual binding is supported by spatiotemporal ‘pointers’ and that pointers are the source of neural signals that track the number of stored items, independent of their content. These content-independent pointers may provide a productive perspective for understanding item-based capacity limits in WM and the role of WM as a gateway for long-term storage.

Posted in: Psycho-physiological bases of engineering , Tagged:

Planning tasks under uncertainty that have a maximum time to be finished

March 6, 2025 10:17 ,

Michal Staniaszek, Lara Brudermüller, Yang You, Raunak Bhattacharyya, Bruno Lacerda, Nick Hawes, Time-bounded planning with uncertain task duration distributions, Robotics and Autonomous Systems, Volume 186, 2025, DOI: 10.1016/j.robot.2025.104926.

We consider planning problems where a robot must gather reward by completing tasks at each of a large set of locations while constrained by a time bound. Our focus is problems where the context under which each task will be executed can be predicted, but is not known in advance. Here, the term context refers to the conditions under which the task is executed, and can be related to the robot’s internal state (e.g., how well it is localised?), or the environment itself (e.g., how dirty is the floor the robot must clean?). This context has an impact on the time required to execute the task, which we model probabilistically. We model the problem of time-bounded planning for tasks executed under uncertain contexts as a Markov decision process with discrete time in the state, and propose variants on this model which allow adaptation to different robotics domains. Due to the intractability of the general model, we propose simplifications to allow planning in large domains. The key idea behind these simplifications is constraining navigation using a solution to the travelling salesperson problem. We evaluate our models on maps generated from real-world environments and consider two domains with different characteristics: UV disinfection, and cleaning. We evaluate the effect of model variants and simplifications on performance, and show that policies obtained for our models outperform a rule-based baseline, as well as a model which does not consider context. We also evaluate our models in a real robot experiment where a quadruped performs simulated inspection tasks in an industrial environment.

Posted in: Robot task planning , Tagged:

RL for multiple tasks in the case of quadrotors and a short state of the art about the general problem

February 20, 2025 10:24 ,

J. Xing, I. Geles, Y. Song, E. Aljalbout and D. Scaramuzza, Multi-Task Reinforcement Learning for Quadrotors, IEEE Robotics and Automation Letters, vol. 10, no. 3, pp. 2112-2119, March 2025, DOI: 10.1109/LRA.2024.3520894.

Reinforcement learning (RL) has shown great effectiveness in quadrotor control, enabling specialized policies to develop even human-champion-level performance in single-task scenarios. However, these specialized policies often struggle with novel tasks, requiring a complete retraining of the policy from scratch. To address this limitation, this paper presents a novel multi-task reinforcement learning (MTRL) framework tailored for quadrotor control, leveraging the shared physical dynamics of the platform to enhance sample efficiency and task performance. By employing a multi-critic architecture and shared task encoders, our framework facilitates knowledge transfer across tasks, enabling a single policy to execute diverse maneuvers, including high-speed stabilization, velocity tracking, and autonomous racing. Our experimental results, validated both in simulation and real-world scenarios, demonstrate that our framework outperforms baseline approaches in terms of sample efficiency and overall task performance. Video is available at https://youtu.be/HfK9UT1OVnY.

Posted in: Applications of reinforcement learning to control engineering, Applications of reinforcement learning to robots , Tagged:

An adaptive KF for estimating angles from IMUs

February 13, 2025 11:51 ,

Zolfa Anvari, Ali Mirhaghgoo, Yasin Salehi, Real-time angle estimation in IMU sensors: An adaptive Kalman filter approach with forgetting factor, Mechatronics, Volume 106, 2025, DOI: 10.1016/j.mechatronics.2024.103280.

In recent years, the applications of Inertial Measurement Unit (IMU) sensors have witnessed significant growth across multiple fields. However, challenges regarding angle estimation using these sensors have emerged, primarily because of the lack of accuracy in accelerometer-based dynamic motion measurements and the associated bias and error accumulation when combined with gyroscope integration. Consequently, the Kalman filter has become a popular choice for addressing these issues, as it enables the sensor to operate dynamically. Despite its widespread use, the Kalman filter requires precise noise statistics estimation for optimal noise cancellation. To accommodate this requirement, adaptive Kalman filter algorithms have been developed for estimating zero-mean Gaussian process matrix (Q) and measurement matrix (R) variances. This study introduces a real-time adaptive approach that employs a forgetting factor to precisely estimate roll and pitch angles in a 6-axis IMU. The study’s novelty lies in its algorithm, which computes the forgetting factor based on the estimation error of the last samples in the sequence. Experimental results for roll angle indicate that, in response to a step change signal, this method achieves a 54%, 39%, and 70% reduction in RMS error relative to the raw sensor data, traditional Kalman filter, and a hybrid adaptive method, respectively. Moreover, this technique exhibits significant improvements in both fixed and sinusoidal conditions for roll and pitch angles, successfully carrying out tasks within required timescales without failures related to computation time.

Posted in: Bayesian filtering , Tagged: ,

On the reasons of the pervasiveness of the myth of meritocracy

February 6, 2025 13:15 ,

Ian R. Hadden, Céline Darnon, Lewis Doyle, Matthew J. Easterbrook, Sébastien Goudeau, Andrei Cimpian, Why the belief in meritocracy is so pervasive, Trends in Cognitive Sciences, Volume 29, Issue 2, 2025, Pages 101-104, DOI: 10.1016/j.tics.2024.12.008.

People worldwide tend to believe that their societies are more meritocratic than they actually are. We propose the belief in meritocracy is widespread because it is rooted in simple, seemingly obvious causal–explanatory intuitions. Our proposal suggests solutions for debunking the myth of meritocracy and increasing support for equity-oriented policies.

Posted in: Cognitive sciences , Tagged:

On the two-ways of learning language in humans: both abstracting detailed knowledge and refining still-only-abstract one

January 9, 2025 16:38 ,

Susan Goldin-Meadow, Inbal Arnon, Whole-to-part development in language creation, Trends in Cognitive Sciences, Volume 29, Issue 1, 2025, Pages 12-14, DOI: 10.1016/j.tics.2024.09.015.

Children approach language by learning parts and constructing wholes. But they can also first learn wholes and then discover parts. We demonstrate this understudied yet impactful process in children creating language without input. Whole-to-part learning thus need not be driven by hard-to-segment input and is a bias that children bring to language.

Posted in: Psycho-physiological bases of engineering , Tagged:

On the use of GPUs for parallelization of MPCs through the parallelization of symbolic mathematical expressions

January 9, 2025 16:29 ,

S. H. Jeon, S. Hong, H. J. Lee, C. Khazoom and S. Kim, CusADi: A GPU Parallelization Framework for Symbolic Expressions and Optimal Control, IEEE Robotics and Automation Letters, vol. 10, no. 2, pp. 899-906, Feb. 2025, DOI: 10.1109/LRA.2024.3512254.

The parallelism afforded by GPUs presents significant advantages in training controllers through reinforcement learning (RL). However, integrating model-based optimization into this process remains challenging due to the complexity of formulating and solving optimization problems across thousands of instances. In this work, we present CusADi , an extension of the casadi symbolic framework to support the parallelization of arbitrary closed-form expressions on GPUs with CUDA . We also formulate a closed-form approximation for solving general optimal control problems, enabling large-scale parallelization and evaluation of MPC controllers. Our results show a ten-fold speedup relative to similar MPC implementation on the CPU, and we demonstrate the use of CusADi for various applications, including parallel simulation, parameter sweeps, and policy training.

Posted in: Computer science , Tagged: , ,

On the limited throughput of the human cognition and its implications, e.g., in Engineering

December 19, 2024 11:49 ,

Jieyu Zheng1, and Markus Meister, The unbearable slowness of being: Why do we live at 10 bits/s?, Neuron (2024), DOI: 10.1016/j.neuron.2024.11.008.

This article is about the neural conundrum behind the slowness of human behavior. The information throughput of a human being is about 10 bits/s. In comparison, our sensory systems gather data at 10 bits/s. The stark contrast between these numbers remains unexplained and touches on fundamental aspects of brain function: what neural substrate sets this speed limit on the pace of our existence? Why does the brain need billions of neurons to process 10 bits/s? Why can we only think about one thing at a time? The brain seems to operate in two distinct modes: the ‘‘outer’’ brain handles fast high-dimensional sensory and motor signals, whereas the ‘‘inner’’ brain processes the reduced few bits needed to control behavior. Plausible explanations exist for the large neuron numbers in the outer brain, but not for the inner brain, and we propose new research directions to remedy this.

Posted in: Psycho-physiological bases of engineering , Tagged: , ,

Survey and benchmarking of open-source, low-cost LLMs for generating program code

December 12, 2024 16:41 ,

Jessica López Espejel, Mahaman Sanoussi Yahaya Alassan, Merieme Bouhandi, Walid Dahhane, El Hassane Ettifouri, Low-cost language models: Survey and performance evaluation on Python code generation, Engineering Applications of Artificial Intelligence, Volume 140, 2025, DOI: 10.1016/j.engappai.2024.109490.

Large Language Models (LLMs) have become a popular choice for many Natural Language Processing (NLP) tasks due to their versatility and ability to produce high-quality results. Specifically, they are increasingly used for automatic code generation to help developers tackle repetitive coding tasks. However, LLMs’ substantial computational and memory requirements often make them inaccessible to users with limited resources. This paper focuses on very low-cost models which offer a more accessible alternative to resource-intensive LLMs. We notably: (1) propose a thorough semi-manual evaluation of their performance in generating Python code, (2) introduce a Chain-of-Thought (CoT) prompting strategy to improve model reasoning and code quality, and (3) propose a new dataset of 60 programming problems, with varied difficulty levels, designed to extend existing benchmarks like HumanEval and EvalPlus. Our findings show that some low-cost compatible models achieve competitive results compared to larger models like ChatGPT despite using significantly fewer resources. We will make our dataset and prompts publicly available to support further research.

Posted in: Artificial Intelligence , Tagged:

A novel safety-critical robotic architecture

December 12, 2024 16:19 ,

Manuel Schrick, Johannes Hinckeldeyn, Marko Thiel, Jochen Kreutzfeldt, A microservice based control architecture for mobile robots in safety-critical applications, Robotics and Autonomous Systems, Volume 183, 2025, DOI: 10.1016/j.robot.2024.104795.

Mobile robots have become more and more common in public space. This increases the importance of meeting safety requirements of autonomous robots. Simple mechanisms, such as emergency braking, alone do not suffice in these highly dynamic situations. Moreover, actual robotic control approaches in literature and practice do not take safety particularly into account. A more sophisticated situational approach for assessment and planning is needed as part of the high-level process control. This paper presents the concept of a safety-critical Robot Control Architecture for mobile robots based on microservices and a Hierarchical Finite State Machine. It expands already existing architectures by drastically reducing the amount of centralized logic and thus increasing the overall system’s level of concurrency, interruptibility and fail-safety. Furthermore, it introduces new potential for code reuse that allows for straightforward implementation of safety mechanisms such as internal diagnostics systems. In doing so, this concept presents the template of a new type of state machine implementation. It is demonstrated with the application of a delivery robot, which was implemented and operated in real public during a broader research project.

Posted in: Robotic architectures