The field of robot learning has made great advances in developing behaviour learning methodologies capable of learning policies for tasks ranging from manipulation to locomotion. However, the problem of combined learning of behaviour and robot structure, here called co-adaptation, is less studied. Most of the current co-adapting robot learning approaches rely on model-free algorithms or assume to have access to an a-priori known dynamics model, which requires considerable human engineering. In this work, we investigate the potential of combining model-free and model-based reinforcement learning algorithms for their application on co-adaptation problems with unknown dynamics functions. Classical model-based reinforcement learning is concerned with learning the forward dynamics of a specific agent or robot in its environment. However, in the case of jointly learning the behaviour and morphology of agents, each individual agent-design implies its own specific dynamics function. Here, the challenge is to learn a dynamics model capable of generalising between the different individual dynamics functions or designs. In other words, the learned dynamics model approximates a multi-dynamics function with the goal to generalise between different agent designs. We present a reinforcement learning algorithm that uses a learned multi-dynamics model for co-adapting robot’s behaviour and morphology using imagined rollouts. We show that using a multi-dynamics model for imagining transitions can lead to better performance for model-free co-adaptation, but open challenges remain.
Investigation into Bio-inspired Snake Robot Designs with Co-Adaptation of Morphology and Behaviour
Riccardo Pretto , Sylvia Cressmann , Shivam Chaubey , Michael Andrew Jansen , Ville Kyrki , and Kevin Sebastian Luck
In 40th Anniversary of the IEEE International Conference on Robotics and Automation (ICRA@40) , 2024
Humans and animals are capable of quickly learning new behaviours to solve new tasks. Yet, we often forget that they also rely on a highly specialized morphology that co-adapted with motor control throughout thousands of years. Although compelling, the idea of co-adapting morphology and behaviours in robots is often unfeasible because of the long manufacturing times, and the need to redesign an appropriate controller for each morphology. In this paper, we propose a novel approach to automatically and efficiently co-adapt a robot morphology and its controller. Our approach is based on recent advances in deep reinforcement learning, and specifically the soft actor critic algorithm. Key to our approach is the possibility of leveraging previously tested morphologies and behaviors to estimate the performance of new candidate morphologies. As such, we can make full use of the information available for making more informed decisions, with the ultimate goal of achieving a more data-efficient co-adaptation (i.e., reducing the number of morphologies and behaviors tested). Simulated experiments show that our approach requires drastically less design prototypes to find good morphology-behaviour combinations, making this method particularly suitable for future co-adaptation of robot designs in the real world.
Bio-inspired robot design considering load-bearing and kinematic ontogeny of chelonioidea sea turtles
Andrew Jansen , Kevin Sebastian Luck , Joseph Campbell , Heni Ben Amor , and Daniel M Aukes
In Biomimetic and Biohybrid Systems: 6th International Conference, Living Machines 2017, Stanford, CA, USA, July 26–28, 2017, Proceedings 6 , 30 oct–01 nov 2017