PLENARY TALKS

Perfection is not Nature’s goal. In the 3.8 billion years, Nature has evolved and selected billions of species with those characteristics that result more suitable for their environment and more likely to survive and to sustain life for generations. Nature is adaptive, constantly learning, and evolving: Nature focuses on evolution. Thus, why are roboticists looking at natural organisms for a robot revolution?

In a constantly changing world, natural organisms’ life and evolution strategies can provide engineers with the rules to design and develop novel embodiments, adaptive behaviours, and new manufacturing processes that are the keys for artificial machines to adapt to unstructured and even dangerous environments.

Natural systems’ secret lies in the smart characteristics of how their body is designed, in how their intelligence is embodied and distributed, and in the synergies that they create each other to effectively adapt, grow and survive. Systems abilities such as growing, morphing, perception-based behaviour, climbing, versatile gripping, highly dexterous manipulation, anchoring, adhesion, are of paramount interest in robotics. Such abilities would allow going beyond the boundaries of the performances of current machines and would open to crosswise technologies for general purpose applications in unstructured environments.

With these premises, I will present our approach to bioinspiration based on the investigation of plants and soft animals’ features, with the double goal to identify and extract the key principles underlying these biological functions and to translate them in a technological solution, and to improve scientific knowledge on these biological systems that we take as models.

Barbara Mazzolai is Associate Director for Robotics and Director of the Bioinspired Soft Robotics Laboratory at the Istituto Italiano di Tecnologia (IIT). From February 2011 to March 2021 she was the Director of the IIT Center for Micro-BioRobotics (CMBR). Her research activities are in the areas of biologically-inspired robotics and soft robotics. In this context, she is the pioneer of the fields of plant-inspired robots and growing robots.

She is a member of the Scientific Advisory Board of the Max Planck Institute for Intelligent Systems (Tübingen and Stuttgart, Germany), since 2016; and a member of the Advisory Committee of the Cluster on Living Adaptive and Energy-autonomous Materials Systems – livMatS (Freiburg, Germany), since 2019. In 2017, she was a Visiting Faculty of the Aerial Robotics Lab at Imperial College London. In 2020, she obtained the Italian National Scientific Qualification of Full Professor in Bioengineering.  She is the author and co-author of more than 270 papers that appeared in international journals, books, and conference proceedings.

Biohybrid systems are often thought of as the result of biomimetics or bio-inspired approaches to find novel solutions for outstanding engineering problems. Advanced capabilities in the military, civilian or medical domains based on biological design principles – with and without AI support – have already started to generate impact on society, specifically in the medical sector when it comes to brain-machine or brain-computer interfaces. Best examples include prosthetic limb control in amputees, but also some sensory augmentations to enhance vision, hearing and haptics, potentially beyond the natural human performance.

The conventional view is, that biological principles inform engineering. But the interdisciplinary venture between modern life-sciences and engineering is by no means a one-way affair. It often sparks a spiral of progress where advances in the understanding of biological design principles spins off novel engineering solutions which may, in turn, enable the development of novel, often more effective, tools to study biological systems. For instance, to test hypotheses and model predictions based on experimental and theoretical work on animals, autonomous robotic systems provide an excellent platform for proof-of-concept studies either confirming or rejecting ideas on how biological systems achieve complex sensorimotor control tasks. Micro air vehicles (MAVs or drones) make a good example. Their flight states may be sensed by inertial measurement units, which are commonly used in the engineering domain to produce feedback for aerodynamic stability, complemented by bio-inspired – or better, bioprincipic – vision sensors which add substantial robustness to attitude control in the low dynamic range.

Another approach is to use autonomous robotic systems to study biological sensorimotor control design in ways that are not possible by methodologies applied in natural sciences. A limiting factor in studies of the neuronal mechanisms underlying sensorimotor control often is that the animals under study have to be fixed in place to enable stable measurements of nerve cells involved in sensing and the generation of control signal driving the various motor systems. Under natural conditions, all sensory systems would be stimulated together and the signals of several modalities are integrated before they control the behaviour.

Over the last couple of years we have developed and advanced the capabilities of a fly-robot-interface, FRI, where neuronal signals of interneurons measuring visual image shift, optic flow, are used to avoid collisions of a 2-wheeled robot manoeuvring in an experimental arena. Using this biohybrid experimental platform under closed-loop conditions, we found that the signal amplitude of fly optic flow processing interneurons depends linearly on the distance to visual objects when the FRI moves on an oscillatory trajectory with a fixed turning radius. Our finding enables us to use the FRI for studies on the significance of active vision as well as the integration of signals from different modalities sensing air flow, (yaw) rotation rates and visual motion. Adding to the FRI platform a device for monitoring the intended movements of the fly by measuring the wing beat amplitude difference between the left and right-wing, allows us to address yet another unsolved problem in animal and human sensorimotor control: How are goal-directed behaviours possible given that stabilization reflexes are permanently active during locomotion.

In my presentation, I will review earlier work as well as recent approaches to improve the capability of our FRI and discuss possible future directions towards a bio-hybrid system

Holger G Krapp is Professor of Systems Neuroscience in the Department of Bioengineering at Imperial College London, UK. He obtained his Diploma in Biology from the University of Tübingen, Germany, in 1992. In 1995 he was awarded his Dr rer nat (PhD) degree for studying the neuronal mechanisms of optic flow processing at the Max-Planck Institute for Biological Cybernetics, Tübingen, Germany. During his first postdoctoral position at the same Institute he continued working on theoretical aspects and the neuronal basis of visual self-motion estimation. In 1996 Holger moved to the California Institute of Technology in Pasadena, USA, working on biophysical mechanisms of neuronal multiplication supporting visually guided behaviour in locust. From 1997 to 2000 he was research fellow at Bielefeld University, Germany, where he continued his work on adaptations to optic flow processing in the visual system. When he accepted a temporary position as lecturer in Sensory Neuroscience in the Department of Zoology, at the University of Cambridge, UK, Holger became interested in multisensory mechanisms of behavioural control. In 2005 he joined Imperial College London as a senior lecturer, where he was promoted to reader and professor in 2009 and 2015, respectively. His current research applies a systems level approach that combines experimental studies with model simulations and robotics to understand the biological design principle of fight and gaze control in flying insects.

The goal of our research is to model animal locomotion systems using computational neuromechanics and then apply their designs and even their materials to robots to improve their mechanical designs, autonomous behaviors, and locomotion. This presentation summarizes our efforts over the last several decades and describes our recent work in more detail. In particular, collaborative work will be presented on how an animal’s length and time characteristics strongly affect its mechanics and control system. We use bioinspiration or biomimicry depending on our specific goals.  Using bioinspiration we have applied the fundamental principles of insect locomotion to develop robots using existing technologies and in a simplified manner. Their motor control is also simplified and the agility of these vehicles makes them suitable for some applications. This approach has been used to develop fast running vehicles and a small fixed-wing vehicle that flies, lands and crawls. Using biomimicry, we are developing other robots and animal models including moth-like compliant, flapping wings that mimic those of the animal. We have developed a number of robots with multi-segmented legs mirroring those of animals. For example, Drosophibot is a dynamically scaled up model of a fruit fly and Puppy is a model of a greyhound with artificial muscles. For these robots, we are developing synthetic nervous systems (SNS) for their control based upon animal neurobiology. We are also developing structurally soft robots, which crawl via peristaltic waves. Robots with a human in the loop for basic control decisions are limited in their movements in complex terrain because of limited communications. Some autonomy is essential for their agility. Insect neurobiology and behavioral experiments are being used to develop SNS navigation systems and decision-making strategies.  Our autonomous vehicles benefit from a distributed control architecture similar to that found in animals, and will eventually implement an animal-inspired SNS brain.

Roger D. Quinn is the Arthur P. Armington Professor of Engineering at Case Western Reserve University.  He is a Fellow of ASME. He won the CWRU University Distinguished Research Award in 2019. He has directed CWRU Biologically Inspired Robotics since its inception in 1990 and graduated approximately 100 graduate students in the field, some of whom have reached leadership positions in industry and academics.  His research, in collaboration with biologists including Profs. Roy Ritzmann and Hillel Chiel, is devoted to the development of robots and control strategies based upon biological principles and modeling animal neuromechanical systems. Dozens of robots have been developed to either improve robot performance with biological principles or model animal systems to better understand them. He has authored more than 300 full-length publications and 9 patents on practical devices resulting from his work.  His biology-engineering collaborative work on behavior based distributed control, robot autonomy, human-machine interfacing, soft robots, and neural control systems have each earned awards. 

As robots move beyond manufacturing applications to less predictable environments, they can increasingly benefit, as animals do, from integrating sensing and control with the passive properties provided by particular combinations and arrangements of materials and mechanisms. This realization is partly responsible for the recent proliferation of soft and bioinspired robots. Tuned materials and mechanisms can provide several kinds of benefits, including energy storage and recovery, increased physical robustness, and decreased response time to sudden events. Further, they may offer passive “open loop” behaviors, and responses to external changes in loading or environmental conditions. Collectively, these properties can also increase the stability of a robot as it interacts with the environment and allow the closed-loop controller to reduce the apparent degrees of freedom subject to control. The design of appropriate materials and mechanisms remains a challenging problem; bioinspiration, genetic algorithms, and numerical shape and materials optimization are all applicable. New multi-material fabrication processes are also steadily increasing the range and magnitude of passive properties available for intrinsically responsive robots.

Mark Cutkosky is the Fletcher Jones Chair in Mechanical Engineering at Stanford University. He applies analyses, simulations, and experiments to the design and control of robotic hands, tactile sensors, and devices for human/computer interaction. In manufacturing, his work focuses on design tools for rapid prototyping.