When followed (the modular approach), the integration effort is removed and the critical section reduced significantly. However, although the process of building robots, and particularly, the integration of new robot modules is simplified, the task of programming robots remains cumbersome. New modules, although interoperate, need to be introduced in the logic of the system manually. This implies that for each module addition or modification, a complete review of the logic that governs the behavior of such robot will need to happen. In other words, the adaptation capabilities of these systems are still limited.

Section c) of the Figure above illustrates the Modular And Self-Adaptable (MASA) approach for building robots. This approach radically changes the robot building process in which, rather than programming, modular robots train themselves for a pre-defined task. By continuously integrating the information from its modules, based on an information model such as the one described by Zamalloa et al. (2018) [2], a robot is able to adapt automatically when new modules are added. This approach reduces both the human development effort and time significantly. The process of building a robot gets simplified to defining a task, adding robot hardware modules and letting the robot train until it accomplishes the assigned task.

(MASA) This approach radically changes the robot building process in which, rather than programming, modular robots train themselves for a pre-defined task.

Introducing the Modular And Self-Adaptable (MASA) approach for building robots:

Represents the Modular And Self-Adaptable (MASA) approach for building robots. The critical section is underlined in red. Step 4, automatic training contains a dashed line implying that this process executes automatically and without any human effort.

The figure above depicts the MASA approach for building robots. Its critical section, much smaller than other approaches’, has been highlighted in red. Similar to what Brooks (1986)[3] proposed, MASA presents a mechanism to incrementally build intelligence for a given task. In the same report, Brooks argues that roboticists typically assume static environments however real-world scenarios involve dynamism. We argue that within these dynamic changes, robots are subject to errors in their measurements, and ultimately, related to their imperfect sensing capabilities.

The MASA strategy for building robots can be summarized as follows:

Buy module: This step refers to the action of acquiring those robot modules required to build the robot. Since the devices are modules, they are assumed to be interoperable, easy to integrate and re-use. Define task (critical section): This step refers to the process of defining the goal that the robot should accomplish in a mathematical form so that the learning algorithms can be based on it. Typically, in Reinforcement Learning (RL) –a set of AI techniques– this mathematical expression is captured in what is called a ‘reward function’ that steers the learning process of the robot. Robot assembly (critical section): We capture the physical construction of the robot in this step, which is simplified since all modules interoperate. Automatic training (critical section): In this step, together with reconfiguration mechanisms such as those proposed by Mayoral et al. (2017)[1]; Zamalloa et al. (2018)[2], we implement AI techniques that allow the robot to continuously integrate the information from its modules and adapt dynamically a neuromorphic model to fit the task defined in previous steps. This way, regardless of the physical changes that happen in the robot (such as additions or removal of modules), the robot will automatically retrain itself for the task. In our particular implementation, we extend the Deep Reinforcement Learning (DRL) techniques proposed by Kojcev et al. (2018)[4] and include the reconfiguration ideas cited previously. Specifically, we apply Proximal Policy Optimization (PPO) Schulman et al. (2017)[5], which alternates between sampling data trough interaction with the environment and optimizing the ‘surrogate’ objective by clipping the policy probability ratio. Noise is introduced on each iteration of the learning algorithm. Deploy: Once trained, this approach outputs a flag that notifies about the success or failure of the automatic training step. In the case of failure, the user can refine the task definition (step 2) or add additional modules to the robot (step 3) and allow the training process to iterate again automatically (step 4) until success.

Depicts the 3 Degrees-of-Freedom (DoF) real robot in a SCARA configuration

Preliminary results

Pictures the 3 Degrees-of-Freedom (DoF) simulated robot in a SCARA configuration

We present an experiment that aims to shed some light into the relevance of this new approach for building robots meant for real-world scenarios (subject to noise and errors) and on the spot testing. We compare the results obtained by the traditional approach and the MASA one on a given task. The task of the robot is to reach a given point in the workspace. The setup consists of a robot with 3 Degrees-of-Freedom (DoF) in a SCARA configuration. The robot is built and configured by following the traditional and MASA approaches. The configuration of each robot follows from its building process and is either programmed or trained. Simulation is used to accelerate the process of experimentation applying, when appropriate, faster than realtime techniques as introduced by Kojcev et al. (2018)[4]. In a first experiment, the robot is built and programmed using traditional control theory mechanisms and is subject to Gaussian noise (N(0, σ)) introduced on each of its joints to simulate the imperfect sensing capabilities. In a second experiment, a modular robot is trained with motor joint observations that pass through a similar Gaussian filter introducing noise on each iteration. A summary of the results is presented in the Table below:

Displays the standard deviation (σ) of the Gaussian noise (N(0, σ)) applied to each one of the joints in the robot and the corresponding Root Mean Square Error (RMSE) obtained while the robot executes the task when following the MASA approach and the traditional approach. Best result per noise perturbation has been highlighted in bold.

Results show that the new approach proposed for building robots outperforms the traditional one in the presence of noise by even an order of magnitude in some cases.