Diversity-based design assist for large legged robots

Abstract

We combine MAP-Elites and highly parallelisable simulation to explore the design space of a class of large legged robots, which stand at around 2m tall and whose design and construction is not wellstudied. The simulation is modified to account for factors such as motor torque and weight, and presents a reasonable fidelity search space. A novel robot encoding allows for bio-inspired features such as legs scaling along the length of the body. The impact of three possible control generation schemes are assessed in the context of body-brain co-evolution, showing that even constrained problems benefit strongly from coupling-promoting mechanisms. A two stage process in implemented. In the first stage, a library of possible robots is generated, treating user requirements as constraints. In the second stage, the most promising robot niches are analysed and a suite of human-understandable design rules generated related to the values of their feature variables. These rules, together with the library, are then ready to be used by a (human) robot designer as a Design Assist tool. CCS CONCEPTS • Computer systems organization → Evolutionary robotics; • Theory of computation → Evolutionary algorithms; • Applied computing → Computer-aided design;

EvoRobogami: Co-designing with Humans in Evolutionary Robotics Experiments

“…Human input can be integrated into an Evolutionary Algorithm (EA) in several ways. For example, expert knowledge can be used to set parameter limits and to design fitness functions [17]. Typically, the only feedback the user receives on their choices is at the end of the experiment, when solutions can be analysed to see if these settings produced the intended results.…”