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Sensors enable autonomous systems to obtain information about their internal states and the environment for guiding their actions. It is as essential for these sensors to reject disturbances as to gather the correct information. There are numerous trade-offs and considerations in designing these sensory systems. For instance, natural agents evolved a vast diversity of highly optimized sensory organs to perform their tasks. This work focuses on how these sensory systems estimate mechanical stimuli. We look at some of the strategies and design principles found in nature to understand fundamental trade-offs and design considerations when acquiring and processing mechanical information.

In teleoperated Robot-Assisted Minimally-Invasive Surgery (RAMIS), a surgeon controls the movements of instruments inside the patient’s body via a pair of robotic joysticks. RAMIS has transformed many surgical disciplines, but its full potential is still to be realized. In this chapter we propose a pathway towards overcoming several bottlenecks that are related to transparency and stability of the teleoperation channels that mediate RAMIS. We describe the traditional system centered and the more recent human-centred approaches to teleoperation, and the special considerations for RAMIS as an application of teleoperation. However, the human-centered approach is still one sided view focusing on the surgeon but neglecting the learning capabilities of robotic systems. Hence, we consider a more general idea of physical human-robot orchestration with coevolution of mutual internal representations – of the human and the robot, and discuss it in comparison to human-human collaboration over teleoperated channels.

Morphological computation proposes the idea that in a physical system, certain computational processes can be off-loaded to the body. However, the concept has still eluded serious theoretical quantification attempts, unlike traditional computational theory. This perspective examines the notion of morphological computation from the well established theories of traditional computation and computational complexity, drawing parallels between the two, to understand the differences and similarities. Further, we look at the quantification efforts of morphological computation and attempt to link it to the unexplored field of control complexity. We argue that the development of complexity theory for control problems is necessary to study and utilize the concept of morphological computation, if it is possible.

Series elastic actuation that takes inspiration from biological muscle-tendon units has been extensively studied and used to address the challenges (e.g. energy efficiency, robustness) existing in purely stiff robots. However, there also exists another form of passive property in biological actuation, parallel elasticity within muscles themselves, and our knowledge of it is limited: for example, there is still no general design strategy for the elasticity profile. When we look at nature, on the other hand, there seems a universal agreement in biological systems: experimental evidence has suggested that a concave-upward elasticity behaviour is exhibited within the muscles of animals. Seeking to draw possible design clues for elasticity in parallel with actuators, we use a simplified joint model to investigate the mechanisms behind this biologically universal preference of muscles. Actuation of the model is identified from general biological joints and further reduced with a specific focus on muscle elasticity aspects, for the sake of easy implementation. By examining various elasticity scenarios, one without elasticity and three with elasticity of different profiles, we find that parallel elasticity generally exerts contradictory influences on energy efficiency and disturbance rejection, due to the mechanical impedance shift thus caused. The trade-off analysis between them also reveals that concave parallel elasticity is able to achieve a more advantageous balance than linear and convex ones. It is expected that the results could contribute to our further understanding of muscle elasticity and provide a theoretical guideline on how to properly design parallel elasticity behaviours for engineering systems such as artificial actuators and robotic joints.

© 2016 IOP Publishing Ltd. The intrinsic muscular properties of biological muscles are the main source of stabilization during locomotion, and superior biological performance is obtained with low energy costs. Man-made actuators struggle to reach the same energy efficiency seen in biological muscles. Here, we compare muscle properties within a one-dimensional and a two-segmented hopping leg. Different force-length-velocity relations (constant, linear, and Hill) were adopted for these two proposed models, and the stable maximum hopping heights from both cases were used to estimate the cost of hopping. We then performed a fine-grained analysis during landing and takeoff of the best performing cases, and concluded that the force-velocity Hill-type model is, at maximum hopping height, the most efficient for both linear and segmented models. While hopping at the same height the force-velocity Hill-type relation outperformed the linear relation as well. Finally, knee angles between 60° and 90° presented a lower energy expenditure than other morphologies for both Hill-type and constant relations during maximum hopping height. This work compares different muscular properties in terms of energy efficiency within different geometries, and these results can be applied to decrease energy costs of current actuators and robots during locomotion.

Robotic researchers have been greatly inspired by the human hand in the search to design and build adaptive robotic hands. Especially, joints have received a lot of attention upon their role in maintaining the passive compliance that gives the fingers flexibility and extendible motion ranges. Passive compliance, which is the tendency to be employed in motion under the influence of an external force, is the result of the stiffness and the geometrical constraints of the joints that define the direction of the motion. Based on its building elements, human finger joints have multi-directional passive compliance which means that they can move in multiple axis of motion under external force. However, due to their complex anatomy, only simplified biomechanical designs based on physiological analysis are preferred in present day robotics. To imitate the human joints, these designs either use fixed degree of freedom mechanisms which substantially limit the motion axes of compliance, or soft materials that can deform in many directions but hinder the fingers' force exertion capacities. In order to find a solution that lies between these two design approaches, we are using anatomically correct finger bones, elastic ligaments and antagonistic tendons to build anthropomorphic joints with multi-directional passive compliance and strong force exertion capabilities. We use interactions between an index finger and a thumb to show that our joints allow the extension of the range of motion of the fingers up to 245% and gripping size to 63% which can be beneficial for mechanical adaptation in gripping larger objects.