2018/07/05 by Amy LaViers · 1 citation
Computer Science · Engineering · Mathematics · #Artificial intelligence #Computer science #Data mining #Engineering #Extant taxon #Flexibility (engineering) #Human–computer interaction #Ideal (ethics) #Machine learning #Mathematics #Measure (data warehouse) #Mechanical engineering #Modular Robots and Swarm Intelligence #Natural (archaeology) #Reinforcement Learning in Robotics #Replicate #Robot #Robot Manipulation and Learning #Work (physics) #cs.RO
paper · pdf · doi:10.3390/arts8020067
Rejected from Nature, after review and appeal, July 4, 2018 (submitted May 11, 2018)
arxiv created 2018/07/05 · openalex publication_date 2019/05/23 · arxiv updated 2019/09/20 · openalex created_date 2020/11/23 · openalex updated_date 2026/08/01
Roboticists are trying to replicate animal behavior in artificial systems. Yet, quantitative bounds on capacity of a moving platform (natural or artificial) to express information in the environment are not known. This paper presents a measure for the capacity of motion complexity -- the expressivity -- of articulated platforms (both natural and artificial) and shows that this measure is stagnant and unexpectedly limited in extant robotic systems. This analysis indicates trends in increasing capacity in both internal and external complexity for natural systems while artificial, robotic systems have increased significantly in the capacity of computational (internal) states but remained more or less constant in mechanical (external) state capacity. This work presents a way to analyze trends in animal behavior and shows that robots are not capable of the same multi-faceted behavior in rich, dynamic environments as natural systems.