Active Inference for Integrated State-Estimation, Control, and Learning

This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators. It is based on the active inference framework, prominent in computational neuroscience as a theory of the brain, where behaviour arises from minimizing variational free-energy. The robotic manipulator shows adaptive and robust behaviour compared to state-of-the-art methods. Additionally, we show the exact relationship to classic methods such as PID control. Finally, we show that by learning a temporal parameter and model variances, our approach can deal with unmodelled dynamics, damps oscillations, and is robust against disturbances and poor initial parameters. The approach is validated on the `Franka Emika Panda' 7 DoF manipulator.Comment: 7 pages, 6 figures, accepted for presentation at the International Conference on Robotics and Automation (ICRA) 202

STATISTICAL THINKING FRAMEWORKS AND MODELS COMPARATIVE STUDY IN GLOBAL MODELS (أطر ونماذج التفكير الإحصائي دراسة مقارنة في النماذج العالمية )

The current study aims to trace and analyze statistical thinking frameworks of various types in terms of their origin, types, dimensions, tasks, skills focused on them, hierarchy and sequence of levels, their theoretical and educational reference, The use of technology in its construction and the possibility of integration between all different statistical thinking frameworks or the construction of new frameworks. It relied on the inductive method in the survey of literature, studies and scientific reports that monitored the frameworks of statistical thinking. It also adopted the descriptive approach in analyzing and concluding the relationship between different statistical thinking frameworks Comparing and categorizing them , The most important results of the study that emerged from the frameworks of statistical thinking is different from the educational, statistical or commercial, etc., and most of the frameworks are one-dimensional and a few of them are two-dimensional, and the majority focused on the use of practical issues or tasks to reveal the levels of statistical thinking of learners, Computational thinking within the framework of statistical thinking, he study concluded several conclusions and recommendations. The most prominent of these was that the recent statistical thinking frameworks did not receive such global attention, despite their importance. However, the study recommended the need to increase attention to the frameworks of statistical thinking by developing them and to find a mechanism for integration among them or to build new statistical thinking frameworks

Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning

Model-based fault-tolerant control (FTC) often consists of two distinct steps: fault detection & isolation (FDI), and fault accommodation. In this work we investigate posing fault-tolerant control as a single Bayesian inference problem. Previous work showed that precision learning allows for stochastic FTC without an explicit fault detection step. While this leads to implicit fault recovery, information on sensor faults is not provided, which may be essential for triggering other impact-mitigation actions. In this paper, we introduce a precision-learning based Bayesian FTC approach and a novel beta residual for fault detection. Simulation results are presented, supporting the use of beta residual against competing approaches.Comment: 7 pages, 2 figures. Accepted at the 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes - SAFEPROCESS 202

Active inference for Robot control: A Factor Graph Approach

Active Inference provides a framework for perception,action and learning, where the optimization is done byminimizing the Free-Energy of a system. This paperexplores whether active inference can be used for closedloopcontrol of a 1 degree of freedom robot arm. This isdone by implementing variational message passing onForney-style factor graphs; a probabilistic programmingframework. We show that an active inference controllerwith variational message passing can perform stateestimation and control at the same time

A feasibility study on using surface treated kaolin mineral in thermoplastic composites

Glass fiber reinforced plastics are the current state of the art, mainly in automotive manufacturing applications. With the industry’s trend towards weight reduction for fuel economy and enhanced performance, an improvement in material composition is needed. This thesis investigates the possibility of using surface-treated kaolin mineral fillers as a substitute or a complement to glass fibers in nylon composites. An experimental approach is utilized to determine their properties and compare them to existing materials in the industry. Specimens are fabricated using a twin-screw tabletop micro-extruder for the first phase of the study, and a 75-ton injection molding machine, which compares to the industrial standard, for the second phase. Mechanical tests, thermal tests, and density measurements are used to evaluate the composite material properties. SEM imaging is used to investigate filler morphology and its distribution within the matrix, as well as voids and defects. A comparison between lab-scale and scale-up fabrication techniques is used to highlight the effects of manufacturing conditions. A micromechanical model is employed to compare experimental results to theoretical predictions. Results suggest that high aspect ratio fillers increase the strength and stiffness of the composite. Conversely, low aspect ratio particles improve impact strength. Furthermore, particle size affects the dispersion of the mineral, with minerals predicted to have a smaller particle size showing less agglomeration. Blending multiple mineral morphologies showed a synergistic effect in resulting properties. Similar trends were found in both phases of the study indicating that lab scale fabrication can be used as a screening to investigate the reinforcing ability of the minerals.M.S

On solving a Stochastic Shortest-Path Markov Decision Process as probabilistic inference

Previous work on planning as active inference addresses finite horizon problems and solutions valid for online planning. We propose solving the general Stochastic Shortest-Path Markov Decision Process (SSP MDP) as probabilistic inference. Furthermore, we discuss online and offline methods for planning under uncertainty. In an SSP MDP, the horizon is indefinite and unknown a priori. SSP MDPs generalize finite and infinite horizon MDPs and are widely used in the artificial intelligence community. Additionally, we highlight some of the differences between solving an MDP using dynamic programming approaches widely used in the artificial intelligence community and approaches used in the active inference community.

Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning

Model-based fault-tolerant control (FTC) often consists of two distinct steps: fault detection & isolation (FDI), and fault accommodation. In this work we investigate posing fault-tolerant control as a single Bayesian inference problem. Previous work showed that precision learning allows for stochastic FTC without an explicit fault detection step. While this leads to implicit fault recovery, information on sensor faults is not provided, which may be essential for triggering other impact-mitigation actions. In this paper, we introduce a precision-learning based Bayesian FTC approach and a novel beta residual for fault detection. Simulation results are presented, supporting the use of beta residual against competing approaches.Team Riccardo Ferrar

Towards Stochastic Fault-Tolerant Control Using Precision Learning and Active Inference

This work presents a fault-tolerant control scheme for sensory faults in robotic manipulators based on active inference. In the majority of existing schemes a binary decision of whether a sensor is healthy (functional) or faulty is made based on measured data. The decision boundary is called a threshold and it is usually deterministic. Following a faulty decision, fault recovery is obtained by excluding the malfunctioning sensor. We propose a stochastic fault-tolerant scheme based on active inference and precision learning which does not require a priori threshold definitions to trigger fault recovery. Instead, the sensor precision, which represents its health status, is learned online in a model-free way allowing the system to gradually, and not abruptly exclude a failing unit. Experiments on a robotic manipulator show promising results and directions for future work are discussed.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Robot DynamicsTeam Riccardo Ferrar

Towards Stochastic Fault-Tolerant Control Using Precision Learning and Active Inference

This work presents a fault-tolerant control scheme for sensory faults in robotic manipulators based on active inference. In the majority of existing schemes a binary decision of whether a sensor is healthy (functional) or faulty is made based on measured data. The decision boundary is called a threshold and it is usually deterministic. Following a faulty decision, fault recovery is obtained by excluding the malfunctioning sensor. We propose a stochastic fault-tolerant scheme based on active inference and precision learning which does not require a priori threshold definitions to trigger fault recovery. Instead, the sensor precision, which represents its health status, is learned online in a model-free way allowing the system to gradually, and not abruptly exclude a failing unit. Experiments on a robotic manipulator show promising results and directions for future work are discussed.</p