Diversity-based Design Assist for Large Legged Robots
This work addresses the design of large legged robots, an area not well-studied, by providing an automated assist tool for human designers, though it appears incremental as it builds on existing methods like MAP-Elites.
The authors tackled the problem of designing large legged robots (around 2m tall) by combining MAP-Elites and parallel simulation to explore the design space, resulting in a library of robots and human-understandable design rules for use as a design assist tool.
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 well-studied. 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.