Bio-inspired soft robotic systems: Exploiting environmental interactions using embodied mechanics and sensory coordination

Despite the widespread development of highly intelligent robotic systems exhibiting great precision, reliability, and dexterity, robots remain incapable of performing basic manipulation tasks that humans take for granted. Manipulation in unstructured environments continues to be acknowledged as a significant challenge. Soft robotics, the use of less rigid materials in robots, has been proposed as one means of addressing these limitations. The technique enables more compliant interactions with the environment, allowing for increasingly adaptive behaviours better suited to more human-centric applications. Embodied intelligence is a biologically inspired concept in which intelligence is a function of the entire system, not only the controller or `brain'. This thesis focuses on the use of embodied intelligence for the development of soft robots, with a particular focus on how it can aid both perception and adaptability. Two main hypotheses are raised: first, that the mechanical design and fabrication of soft-rigid hybrid robots can enable increasingly environmentally adaptive behaviours, and second, that sensing materials and morphology can provide intelligence that assists perception through embodiment. A number of approaches and frameworks for the design and development of embodied systems are presented that address these hypotheses. It is shown how embodiment in soft sensor morphology can be used to perform localised processing and thereby distribute the intelligence over the body of a system. Specifically in soft robots, sensor morphology utilises the directional deformations created by interactions with the environment to aid in perception. Building on and formalising these ideas, a number of morphology-based frameworks are proposed for detecting different stimuli. The multifaceted role of materials in soft robots is demonstrated through the development of materials capable of both sensing and changes in material property. Such materials provide additional functionality beyond their integral scaffolding and static mechanical characteristics. In particular, an integrated material has been created exhibiting both sensing capabilities and also variable stiffness and `tack’ force, thereby enabling complex single-point grasping. To maximise the intelligence that can be gained through embodiment, a design approach to soft robots, `soft-rigid hybrid' design is introduced. This approach exploits passive behaviours and body dynamics to provide environmentally adaptive behaviours and sensing. It is leveraged by multi-material 3D printing techniques and novel approaches and frameworks for designing mechanical structures. The findings in this thesis demonstrate that an embodied approach to soft robotics provides capabilities and behaviours that are not currently otherwise achievable. Utilising the concept of `embodiment' results in softer robots with an embodied intelligence that aids perception and adaptive behaviours, and has the potential to bring the physical abilities of robots one step closer to those of animals and humans.EPSR

An anthropomorphic robotic finger with innate human-finger-like biomechanical advantages part II : flexible tendon sheath and grasping demonstration

The human hand has a fantastic ability to interact with various objects in the dynamic unstructured environment of our daily activities. We believe that this outstanding performance benefits a lot from the unique biological features of the hand musculoskeletal system. In Part I of this article, a bio-inspired anthropomorphic robotic finger was developed, based on which two human-finger-like biomechanical advantages were elaborately investigated, including the anisotropic variable stiffness associated with the ligamentous joints and the enlarged feasible force space associated with the reticular extensor mechanisms. In Part II, the fingertip force-velocity characteristics resulting from the flexible tendon sheath are studied. It indicates that the fingertip force–velocity workspace can be greatly augmented owing to the self-adaptive morphing of the flexible tendon sheaths, showing the average improvement of 41.2% theoretically and 117.5% experimentally compared with the results of 2 mm, 4 mm, and 6 mm size rigid tendon sheaths. Grasping tests and comparisons are then conducted with four three-fingered robotic hands (one with the robotic finger proposed in Part I, one with hinge joints, one with linear extensors, and one with rigid tendon sheaths) and the human hands of six subjects to handle various objects on flat, rough, and soft surfaces. The results show that the novel bio-inspired design in this research could improve the grasping success rates of the robotic hand. Compared with the grasping test results from the robotic hand with the bio-inspired robotic finger proposed in Part I, the overall grasping performance of a robotic hand with hinge joints, linear extensors, and rigid tendon sheaths decreases by 10%, 6%, and 17%, respectively. The results have also shown that with the embedded biomechanical advantages, even without complex control and sensory systems, the robotic fingers can achieve very comparable performance to human fingers in the grasping demonstrations presented, indicating average 94% of the success rate achieved by the human fingers. Successfully demonstrating 14 of 16 grasp types in the Cutkoskey taxonomy further shows the human-finger-like grasping capability of the proposed robotic fingers

How to build a biological machine using engineering materials and methods

We present work in 3D printing electric motors from basic materials as the key to building a self-replicating machine to colonise the Moon. First, we explore the nature of the biological realm to ascertain its essence, particularly in relation to the origin of life when the inanimate became animate. We take an expansive view of this to ascertain parallels between the biological and the manufactured worlds. Life must have emerged from the available raw material on Earth and, similarly, a self-replicating machine must exploit and leverage the available resources on the Moon. We then examine these lessons to explore the construction of a self-replicating machine using a universal constructor. It is through the universal constructor that the actuator emerges as critical. We propose that 3D printing constitutes an analogue of the biological ribosome and that 3D printing may constitute a universal construction mechanism. Following a description of our progress in 3D printing motors, we suggest that this engineering effort can inform biology, that motors are a key facet of living organisms and illustrate the importance of motors in biology viewed from the perspective of engineering (in the Feynman spirit of "what I cannot create, I cannot understand")

Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense

Recent progress in deep learning is essentially based on a "big data for small tasks" paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a "small data for big tasks" paradigm, wherein a single artificial intelligence (AI) system is challenged to develop "common sense", enabling it to solve a wide range of tasks with little training data. We illustrate the potential power of this new paradigm by reviewing models of common sense that synthesize recent breakthroughs in both machine and human vision. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense. When taken as a unified concept, FPICU is concerned with the questions of "why" and "how", beyond the dominant "what" and "where" framework for understanding vision. They are invisible in terms of pixels but nevertheless drive the creation, maintenance, and development of visual scenes. We therefore coin them the "dark matter" of vision. Just as our universe cannot be understood by merely studying observable matter, we argue that vision cannot be understood without studying FPICU. We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning. In summary, we argue that the next generation of AI must embrace "dark" humanlike common sense for solving novel tasks.Comment: For high quality figures, please refer to http://wellyzhang.github.io/attach/dark.pd

Intelligence by Design: Principles of Modularity and Coordination for Engineerin

All intelligence relies on search --- for example, the search for an intelligent agent's next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dissertation describes an approach, Behavior-Oriented Design (BOD) for engineering complex agents. A complex agent is one that must arbitrate between potentially conflicting goals or behaviors. Behavior-oriented design builds on work in behavior-based and hybrid architectures for agents, and the object oriented approach to software engineering. The primary contributions of this dissertation are: 1.The BOD architecture: a modular architecture with each module providing specialized representations to facilitate learning. This includes one pre-specified module and representation for action selection or behavior arbitration. The specialized representation underlying BOD action selection is Parallel-rooted, Ordered, Slip-stack Hierarchical (POSH) reactive plans. 2.The BOD development process: an iterative process that alternately scales the agent's capabilities then optimizes the agent for simplicity, exploiting tradeoffs between the component representations. This ongoing process for controlling complexity not only provides bias for the behaving agent, but also facilitates its maintenance and extendibility. The secondary contributions of this dissertation include two implementations of POSH action selection, a procedure for identifying useful idioms in agent architectures and using them to distribute knowledge across agent paradigms, several examples of applying BOD idioms to established architectures, an analysis and comparison of the attributes and design trends of a large number of agent architectures, a comparison of biological (particularly mammalian) intelligence to artificial agent architectures, a novel model of primate transitive inference, and many other examples of BOD agents and BOD development

A biologically inspired soft robotic hand using chopsticks for grasping tasks

In this paper we investigate the dexterity of human manipulation capabilities by using a soft robotic hand. We built a robotic hand based on our inspiration from the real human’s, which is capable of handling chopsticks for grasping variations of objects. The robotic hand is made of soft structures, by using anthropomorphic configurations of bones, joints, ligaments, and tendons, that are connected to a minimum set of motor components, i.e. only four servomotors. By developing a minimalistic physics model of chopstick handling and its simulation experiments, we have identified one of the necessary conditions of actuation which enables the robot to grasp variations of small objects, those with different shape, size and weight

Proceedings of the 8th international conference on disability, virtual reality and associated technologies (ICDVRAT 2010)

The proceedings of the conferenc