Since December 2014, FourByThree Project (“Highly customizable robotic solutions for effective and safe human robot collaboration in manufacturing applications”) is developing a new generation of modular industrial robotic solutions that are suitable for efficient task execution in collaboration with humans in a safe way and are easy to use and program by factory workers. This paper summarizes the key technologies that are used to achieve this goal.

This paper presents the design of the robot AILA, a mobile dual-arm robot system developed as a research platform for investigating aspects of the currently booming multidisciplinary area of mobile manipulation. The robot integrates and allows in a single platform to perform research in most of the areas involved in autonomous robotics: navigation, mobile and dual-arm manipulation planning, active compliance and force control strategies, object recognition, scene representation, and semantic perception. AILA has 32 degrees of freedom, including 7-DOF arms, 4-DOF torso, 2-DOF head, and a mobile base equipped with six wheels, each of them with two degrees of freedom. The primary design goal was to achieve a lightweight arm construction with a payload-to-weight ratio greater than one. Besides, an adjustable body should sustain the dual-arm system providing an extended workspace. In addition, mobility is provided by means of a wheel-based mobile base. As a result, AILA's arms can lift 8kg and weigh 5.5kg, thus achieving a payload-to-weight ratio of 1.45. The paper will provide an overview of the design, especially in the mechatronics area, as well as of its realization, the sensors incorporated in the system, and its control software.

This paper presents the use of context-based predictions for the selection and on-line modification of the compliance of a robot manipulator. The work is partially inspired on current neuroscience hypotheses about the control of the human arm and the computational processes used by the brain. A first experiment uses inspiration from the classical neuroscience experiment of the Waiter Task. In the original experiment, the non-dominant human arm is holding a weight of 1 Kg. When this weight is unloaded by a self-generated action (with the dominant arm), it is observed that the non-dominant arm does not suffer perceptible postural changes. The reason arguably stems from the prediction of the forces occurring at the unloading, since the inherently delayed sensory feedback present in biological systems would not suffice to react in such a short notice as observed. The experiment is reproduced in a robotic platform by means of forward models and compliance adaption via stiffness control as speculated in neuroscience hypotheses. A second experiment uses context-based predictions to modify on-line the compliance of the robot manipulator. For this task, a Bayesian predictor in the form of a Relevance Vector Machine combines the use of prior knowledge and expected sensory feedback to correct for an erroneous compliance in the case of a falsely-predicted context. The results are combined in an architecture called Predictive Context-Based Adaptive Compliance (PCAC).

This paper describes the design and control methodology used for the development of a dual-arm manipulator as well as its deployment in a production scenario. Multi-modal and sensor-based manipulation strategies are used to guide the robot on its task to supervise and, when necessary, solve faulty situations in a production line. For that task the robot is equipped with two arms, aimed at providing the robot with total independence from the production line. In other words, no extra mechanical stoppers are mounted on the line to halt targeted objects, but the robot will employ both arms to (a) stop with one arm a carrier that holds an object to be inserted/replaced, and (b) use the second arm to handle such object. Besides, visual information from head and wrist-mounted cameras provide the robot with information such as the state of the production line, the unequivocal detection/recognition of the targeted objects, and the location of the target in order to guide the grasp.

Evolving a monolithic solution for complex robotic problems is hard. One of the reasons for this is the difficulty of defining a global fitness function that leads to a solution with desired operating properties. The problem with a global fitness function is that it may not reward intermediate solutions that would ultimately lead to the desired operating properties. A possible way to solve such a problem is to decompose the solution space into smaller subsolutions with lower number of intrinsic dimensions. In this paper, we apply the design principles of behavior based systems to decompose a complex robot control task into subsolutions and show how to incrementally modify the fitness function that (1) results in desired operating properties as the subsolutions are learned, and (2) avoids the need to learn the coordination of behaviors separately. We demonstrate our method by learning to control a quadrocopter flying vehicle.

This paper describes the use of evolutionary algorithms to find an optimal solution for the parameters of an impedance controller represented as an artificial neural network (ANN). An impedance controller with force tracking capabilities has been evolved using evolutionary strategies which control the forces between a robotic manipulator and the environment. Simulation results show the controllerpsilas performance using a model of a two-link robot arm and a Hunt-Crossley non-linear model of the environment.

In recent years, neuroevolutionary methods have shown great promise in solving learning tasks, especially in domains that are stochastic, partially observable, and noisy. In this paper, we show how the Kalman filter can be exploited (1) to efficiently find an optimal solution (i. e. reducing the number of evaluations needed to find the solution), (2) to find solutions that are robust against noise, and (3) to recover or reconstruct missing state variables, traditionally known as state estimation in control engineering community. Our algorithm has been tested on the double pole balancing without velocities benchmark, and has achieved significantly better results on this benchmark than the published results of other algorithms to date.