## 1 Introduction  
Over the past few decades, robots have become extremely prevalent and can be found in various sectors, including automated industrial processes and beyond. As a result, the robotics industry has experienced rapid growth and has become a popular field of research, constantly developing new and innovative robotic applications. Recent reports have emphasised the significance of this industry, estimating the global robotics market's value to be nearly 28 billion dollars in 2020 [1]. Furthermore, forecasts predict that the market value will reach approximately 74 billion dollars by 2026, largely driven by the high demand for robots in industries such as automotive and logistics. This increased interest in robots of all types can be attributed to advancements in automation and robotics, which have made it possible to utilise them in demanding applications while simultaneously reducing operating costs. A series of pre-programmed trajectories are often repeatedly executed by robotic manipulators in a so-called position control scheme. However, this control scheme is ineffective in circumstances where knowledge of the robotic manipulator's configuration does not assure accurate task completion and in situations where a robot is interacting dynamically with its environment. For such situations, force control, also known as interaction control, is required [2]. In such cases, force-torque sensors are used to measure interaction forces and torques in order to interpret the interaction [3; 4]. Additionally, force control is frequently appropriate when the robot collaborates with or interacts with a human. Force control, however, is not advantageous when the robot is moving through open space (i.e., there is no environmental interaction). Therefore, a mix of force and position control is often employed. A force-torque sensor is mounted at the robot's tip in the majority of contemporary applications, i.e., in the wrist of the robotic arm. Measuring interaction forces using standard 6-dimensional (6D) force-torque sensors, however, can occasionally be difficult, particularly when using robots with small payloads and when cost-effectiveness is of high importance. Since the workspace of the robotic manipulator is constrained, it can be expanded by mounting it on a portable platform. Thus, the term _mobile manipulator_ then applies to this type of robot [5; 6]. It is typically convenient to mount small robotic manipulators onto moving platforms due to weight restrictions. However, the payload of these manipulators is frequently insufficient for a force sensor to fit on the end-effector while still allowing them to carry out their intended action. In our earlier research, we suggested the techniques [7; 8] for estimating forces acting on the robot end-effector by detecting forces with a 6D force-torque sensor mounted beneath the robot base. This approach used neural networks for data-driven implicit modelling of robot dynamics. In order to infer the end-effector forces using a neural network, the method used forces obtained by the base-mounted sensor and robot kinematics data (joint positions, velocities, and accelerations) provided by the robot controller. This strategy has the advantage of not consuming any payload and leaving all payload available for a specific task. This study builds on that approach by extending it so that force-torque sensors can be replaced with a more cost-effective alternative in the form of an array of simple 1-dimensional (1D) strain gauges [9]. Thus, this paper aims to prove that this research is viable in real-world setups for the estimation of end-effector forces and robot joint torques. It is based on the hypothesis that a set of 1D strain gauges positioned off-center beneath the robot base can provide sufficient data to reliably and accurately estimate needed physical quantities. The rest of the paper is organised as follows. Section 2 reviews the current state of the art and identifies manuscript contributions in relation to it.