Paleoinspired robotics as an experimental approach to the history of life.

Paleontologists must confront the challenge of studying the forms and functions of extinct species for which data from preserved fossils are extremely limited, yielding only a fragmented picture of life in deep time. In response to this hurdle, we describe the nascent field of paleoinspired robotics, an innovative method that builds upon established techniques in bioinspired robotics, enabling the exploration of the biology of ancient organisms and their evolutionary trajectories. This Review presents ways in which robotic platforms can fill gaps in existing research using the exemplars of notable transitions in vertebrate locomotion.

State transition learning with limited data for safe control of switched nonlinear systems.

Switching dynamics are prevalent in real-world systems, arising from either intrinsic changes or responses to external influences, which can be appropriately modeled by switched systems. Control synthesis for switched systems, especially integrating safety constraints, is recognized as a significant and challenging topic. This study focuses on devising a learning-based control strategy for switched nonlinear systems operating under arbitrary switching law.

[A case of giardiasis diagnosed from a high output stoma after surgery for obstructive colorectal cancer].

This report describes a case of giardiasis detected through stool smear analysis of postoperative stool fluid collected from a high output stoma for obstructive colorectal cancer. The patient, a 67-year-old male, underwent right hemicolectomy with ileostomy for obstructive colorectal cancer. The persistent excessive excretion of postoperative stool fluid from the stoma prompted a stool smear test.

Visuo-dynamic self-modelling of soft robotic systems.

Soft robots exhibit complex nonlinear dynamics with large degrees of freedom, making their modelling and control challenging. Typically, reduced-order models in time or space are used in addressing these challenges, but the resulting simplification limits soft robot control accuracy and restricts their range of motion. In this work, we introduce an end-to-end learning-based approach for fully dynamic modelling of any general robotic system that does not rely on predefined structures, learning dynamic models of the robot directly in the visual space.

Utilising redundancy in musculoskeletal systems for adaptive stiffness and muscle failure compensation: a model-free inverse statics approach.

Vertebrates possess a biomechanical structure with redundant muscles, enabling adaptability in uncertain and complex environments. Harnessing this inspiration, musculoskeletal systems offer advantages like variable stiffness and resilience to actuator failure and fatigue. Despite their potential, the complex structure presents modelling challenges that are difficult to explicitly formulate and control.