Morphological Development at the Evolutionary Timescale: Robotic Developmental Evolution

Pressure-Based Soft Agents

Biological agents have bodies that are composed mostly of soft tissue. Researchers have resorted to soft bodies to investigate Artificial Life (ALife)-related questions; similarly, a new era of soft-bodied robots has just begun. Nevertheless, because of their infinite degrees of freedom, soft bodies pose unique challenges in terms of simulation, control, and optimization. Herein I propose a novel soft-bodied agents formalism, namely, pressure-based soft agents (PSAs): spring-mass membranes containing a pressurized medium. Pressure endows the agents with structure, while springs and masses simulate softness and allow the agents to assume a large gamut of shapes. PSAs actuate both locally, by changing the resting lengths of springs, and globally, by modulating global pressure. I optimize the controller of PSAs for a locomotion task on hilly terrain, an escape task from a cage, and an object manipulation task. The results suggest that PSAs are indeed effective at the tasks, especially those requiring a shape change. I envision PSAs as playing a role in modeling soft-bodied agents, such as soft robots and biological cells.

Enhancing stance robustness and jump height in bipedal muscle-actuated systems: a bioinspired morphological development approach

Recognizing humans' unmatched robustness, adaptability, and learning abilities across anthropomorphic movements compared to robots, we find inspiration in the simultaneous development of both morphology and cognition observed in humans. We utilize optimal control principles to train a muscle-actuated human model for both balance and squat jump tasks in simulation. Morphological development is introduced through abrupt transitions from a 4 year-old to a 12 year-old morphology, ultimately shifting to an adult morphology. We create two versions of the 4 year-old and 12 year-old models- one emulating human ontogenetic development and another uniformly scaling segment lengths and related parameters. Our results show that both morphological development strategies outperform the non-development path, showcasing enhanced robustness to perturbations in the balance task and increased jump height in the squat jump task. Our findings challenge existing research as they reveal that starting with initial robot designs that do not inherently facilitate learning and incorporating abrupt changes in their morphology can still lead to improved results, provided these morphological adaptations draw inspiration from biological principles.