Endotracheal intubation is a mandatory competency for most medical staff. This procedure involves opening the entrance of the patient's upper windpipe using a laryngoscope and then inserting a tube into the windpipe to supply Oxygen to the patient. This time critical intervention requires careful control of the force vector on the tongue to lift it parallel to the jaw than to push the jaw to open the mouth. However, traditional intubation training methods in which novices practice intubation on prostheses lack haptic feedback to improve force control. We designed a sensorised intubation training phantom that can provide trainees with vibrotactile feedback reflecting the laryngoscope's force on the tongue. The critical component of this phantom is a silicon rubber tongue embedded with magnets and hall effect sensors. We calibrated the hall effect sensor readings to predict the force vector exerted on the tongue with errors less than 0.5 N in the lifting and pushing directions. We conducted a controlled experiment, mainly comparing the training results between participants with and without haptic feedback. Results show a statistically significant drop in the undesired forces due to haptic feedback, and the skill is retained when tested after 24 hours without haptic feedback.

A patient would contract surface muscles as a reaction called muscle guarding when experiencing discomfort and pain during physical palpation. This reaction carries important information about an affected location. Training physicians to regulate palpation forces to elicit just enough muscle tension is a challenge using real patients. Tunable stiffness mechanisms enabled by soft robotics can be effectively integrated into medical simulator designs for effective clinical education. In this paper, we propose a controllable stiffness muscle layer to simulate guarding for abdominal palpation training. Designs with soft, fine, and rigid granular jamming, stretchable and non-stretchable layer jamming mechanisms were tested and evaluated as methods to create controllable stiffness muscle. User studies have been carried out on 10 naive participants to differentiate the tense and relaxed abdomen with the proposed jamming mechanisms. Muscle samples made of ground coffee (fine granular jamming) and latex layers (stretchable layer jamming) show good usability in simulating abdomen with different stiffness with at least 75% of the user data exhibits more than 70% of decision accuracy for both tested palpation gestures (single finger and multiple fingers) after short pre-training.

This paper shows analytical and experimental evidence of using the vibration dynamics of a compliant whisker for accurate terrain classification during steady state motion of a mobile robot. A Hall effect sensor was used to measure whisker vibrations due to perturbations from the ground. Analytical results predict that the whisker vibrations will have one dominant frequency at the vertical perturbation frequency of the mobile robot and one with distinct frequency components. These frequency components may come from bifurcation of vibration frequency due to nonlinear interaction dynamics at steady state. Experimental results also exhibit distinct dominant frequency components unique to the speed of the robot and the terrain roughness. This nonlinear dynamic feature is used in a deep multi-layer perceptron neural network to classify terrains. We achieved 85.6% prediction success rate for seven flat terrain surfaces with different textures.

Robotic phantoms enable advanced physical examination training before using human patients. In this article, we present an abdominal phantom for palpation training with controllable stiffness liver nodules that can also sense palpation forces. The coupled sensing and actuation approach is achieved by pneumatic control of positive-granular jammed nodules for tunable stiffness. Soft sensing is done using the variation of internal pressure of the nodules under external forces. This article makes original contributions to extend the linear region of the neo-Hookean characteristic of the mechanical behavior of the nodules by 140% compared to no-jamming conditions and to propose a method using the organ level controllable nodules as sensors to estimate palpation position and force with a root-mean-square error of 4% and 6.5%, respectively. Compared to conventional soft sensors, the method allows the phantom to sense with no interference to the simulated physiological conditions when providing quantified feedback to trainees, and to enable training following current bare-hand examination protocols without the need to wear data gloves to collect data.

Robust mechanisms for slip resistance are an open challenge in legged locomotion. Animals such as goats show impressive ability to resist slippage on cliffs. It is not fully known what attributes in their body determine this ability. Studying the slip resistance dynamics of the goat may offer insight toward the biologically inspired design of robotic hooves. This article tests how the embodiment of the hoof contributes to solving the problem of slip resistance. We ran numerical simulations and experiments using a passive robotic goat hoof for different compliance levels of its three joints. We established that compliant yaw and pitch and stiff roll can increase the energy required to slide the hoof by ≈ 20% compared to the baseline (stiff hoof). Compliant roll and pitch allow the robotic hoof to adapt to the irregularities of the terrain. This produces an antilock braking system-like behavior of the robotic hoof for slip resistance. Therefore, the pastern and coffin joints have a substantial effect on the slip resistance of the robotic hoof, while the fetlock joint has the lowest contribution. These shed insights into how robotic hooves can be used to autonomously improve slip resistance.

It is well acknowledged that the opposing thumb granted humans advanced manipulation capabilities. However, such a feature is not statistically quantified, and its representation is not formally addressed in robotics yet. This paper studies whether the displacement of the opposing thumb in humans is a determining factor for shaping the grip. Using statistical analysis of the variability of motion capture data from the GRASP database, we found that the displacement of the thumb plays a leading role on the shaping of the grip, independently from the specific object being grasped. Furthermore, we map and compare the reachability spaces of the human thumb and two state-of-the-art robotic thumbs: (1) the shadow and (2) the iCub hands. We conclude that the kinematics of robotic thumbs does not evenly span the reachability space of the human thumb, favoring precision grasping motions. Hence, our findings contribute to the discussion of the optimal modeling of robotic hands.

The complexity of soft continuum manipulators with hybrid and tuneable structures poses a challenging task to achieve an inverse kinematics model which is both precise and computationally efficient for control and optimization purposes. In this paper, a new method based on the principle of virtual work and a geometry deformation approach is presented for the inverse kinematics model of the STIFF-FLOP arm which is a pneumatically actuated continuum manipulator. We propose a novel simplified and computationally efficient yet accurate analytical solution to analyse the static behaviour of a compound soft manipulator in the presence of external and body forces which is verified against experimental data, showing promising agreement with 10% mean error for planar movements. In the process, we present a new modelling approach for braided soft extensor actuators with no braid-surface relative slip constraint. For the first time, our model predicts a simple analytical solution for the cross section deformation which is essential to control soft manipulators with regional tunable stiffness structure.

Haptic perception in biological systems not only depends on the environmental conditions, but also on the behavioral state and the internal impedance of the embodiment because proprioceptive sensors are embedded in the muscle and tendons used for actuation. A simple example of such a phenomenon can be found when people are asked to palpate a soft tissue to identify a stiff-inclusion. People tend to perform a variety of palpation strategies depending on their previous knowledge and the desired information. Does this mean that the probing behavioral variables and internal muscle impedance parameters and their interaction with given environmental conditions play a role in the perception information gain during the estimation of soft tissue's properties? In this paper, we use a two-degree of freedom laboratory-made variable stiffness and indentation probe to investigate how the modulation of probing behavioral and internal stiffness variables can affect the accuracy of the depth estimation of stiff inclusions in artificial silicon phantom tissue using information gain metrics based on prior knowledge in form of memory primitives.

The remarkable ability of goats to maintain stability during climbing cliffs or trees provides a valuable opportunity to understand some of the secrets of stable legged locomotion on unstructured terrains. This paper, for the first time, presents analytical and experimental explanations as to how the morphological computation at the goat hoof makes a significant contribution to slip reduction on both smooth and rough surfaces. We conducted experiments using a laboratory made hoof and compared its dynamic behavior against a rounded foot. We recorded forces and position of the hoof to analyze the effect of its shape and the individual contributions from 3-joints in the hoof on the work required to slip. Results state that the work required to move the hoof is more than 3 times that required to move a rounded foot. Additionally, the variables in the transient state are affected not only by the number and type of joints but also by the interaction with the environment. These findings promote the development of new types of feet for robots for all terrain conditions with greater stability and less control complexity.

A novel three axis force sensor, based on magnetic flux measurements, was used in the fingers of a gripper. The force sensor uses three Hall Effect sensors orthogonally placed at the base of a hemisphere made of silicon rubber. A neodymium permanent magnet was inside the hemisphere. When a force was applied to the perimeter of hemisphere, it compressed the hemisphere displacing the magnet. This displacement caused change in the magnetic field around the Hall-effect sensors. By analysing these changes, we calculated the force in three directions using a lookup table. This sensor can be used in robot grippers to manipulate objects dexterously with tactile feedback. The cheap construction, robustness and reliability are few advantages of this sensor for it to be used in industrial applications. The sensor design, simulation and its characterization are presented in this work. Furthermore, as an application, a peg in a hole experiment was carried out to present the ability of the sensors to be used in robot grippers for manipulation tasks.

Mammalian whisker follicle contains multiple sensory receptors strategically organized to capture tactile sensory stimuli of different frequencies via the vibrissal system. There have been a number of attempts to develop robotic whiskers to perform texture classification tasks in the recent past. Inspired by the features of biological whisker follicle, in this paper we design and use a novel soft whisker follicle comprising of two different frequency-dependent data capturing modules to derive deeper insights into the biological basis of tactile perception in the mammalian whisker follicle. In our design, the innervations at the Outer Conical Body (OCB) of a biological follicle are realized by a piezoelectric transducer for capturing high frequency components; whereas the innervations around the hair Papilla are represented by a hall sensor to capture low frequency components during the interaction with the environment. In this paper, we show how low dimensional information such as the principle components of co-variation of these two sensory modalities vary for different speeds and indentations of brushing the whisker against a surface. These new insights into the biological basis of tactile perception using whiskers provides new design guidelines to develop efficient robotic whiskers.

This paper presents salient features of human-human interaction where one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several salient features of the interaction between the guider and follower such as: 1) the order of an autoregressive (AR) control policy that maps states of the follower to actions of the guider; 2) how the guider may modulate the pulling force in response to the trust level of the follower; and 3) how learning may successively apportion the responsibility of control across different muscles of the guider. Based on experimental systems identification on human demonstrations from ten pairs of naive subjects, we show that guiders tend to adopt a third-order AR predictive control policy and followers tend to adopt second-order reactive control policy. Moreover, the extracted guider's control policy was implemented and validated by human-robot interaction experiments. By modeling the follower's dynamics with a time varying virtual damped inertial system, we found that it is the coefficient of virtual damping which is most sensitive to the trust level of the follower. We used these experimental insights to derive a novel controller that integrates an optimal order control policy with a push/pull force modulator in response to the trust level of the follower monitored using a time varying virtual damped inertial model.

Humans can hold a live animal like a hamster without overly squeezing despite the fact that its soft body undergoes impedance and size variations due to breathing and wiggling. Although the exact nature of such biological motor controllers is not known, existing literature suggests that they maintain metastable interactions with dynamic objects based on prediction rather than reaction. Most robotic gripper controllers find such tasks very challenging mainly due to hard constraints imposed on the stability of closed-loop control and inadequate rates of convergence of adaptive controller parameters. This paper presents experimental and numerical simulation results of a control law based on a relaxed stability criterion of reducing the probability of failure to maintain a stable grip on a soft object that undergoes temporal variations in its internal impedance. The proposed controller uses only three parameters to interpret the probability of failure estimated using a history of grip forces to adjust the grip on the dynamic object. Here, we demonstrate that the proposed controller can maintain smooth and stable grip tightening and relaxing when the object undergoes random impedance variations, compared with a reactive controller that involves a similar number of controller parameters.

Materials and textures identification is a desired ability for robots. Developing such systems require tactile sensors that have enough sensitivity and spatial resolution, and the computational intelligence to meaningfully interpret sensor data. This paper introduces a texture classification algorithm utilizing support vector machine (SVM) classifier. Data taken from a novel three axis tactile sensor that utilize magnetic flux measurements for transduction was used to obtain the three dimensional tactile data. Frobenius norm calculated from the covariance matrix of the above data and the mean values of the three dimensional sensor data were used as features. Palpation velocity and small vertical load variances had minimum influence on the proposed algorithm. We have compared this algorithm with two other classification methods. They are: classify using the feature spatial period that is calculated from principal frequencies of the textures/material, and classify using neural network classifier with special properties of each material's tactile signals as features. For eight classes of material, the proposed algorithm performed faster and more accurately than the comparators when the scanning velocity and the vertical load varied.

There has been a growing enthusiasm to use anthropomorphic hands of humanoid robots to manipulate every-day objects and tools designed for humans. However, multi-fingered grasping imposes a formidable control challenge due to the high dimensionality of the joint space and the difficulty to form a functional grip on objects. We propose a hybrid technique based on grasping synergies extracted from kinaesthetic demonstrations on a given object with a primitive geometry - a cuboid in this case - and passive kinematic enveloping as a generalization technique. Experiments were carried out on an iCub humanoid robot using everyday objects such as a telephone receiver, a computer mouse, three white board markers bundled together, a fencing handle, a compact disc keep case, and a drinking glass. We prove that the primitives extracted from kinaesthetic demonstrations on a cuboid can be generalized across a majority of the above real world objects.

This paper presents an innovative approach of utilising visual feedback to determine physical interaction forces with soft tissue during Minimally Invasive Surgery (MIS). This novel force sensing device is composed of a linear retractable mechanism and a spherical visual feature. The sensor mechanism can be adapted to endoscopic cameras used in MIS. As the distance between the camera and feature varies due to the sliding joint, interaction forces with anatomical surfaces can be computed based on the visual appearance of the feature in the image. Hence, this device allows the measurement of forces without introducing new stand-alone sensors. A mathematical model was derived based on validation data tests and preliminary experiments were conducted to verify the model's accuracy. Experimental results confirm the effectiveness of our vision based approach.

Robotic manipulators for Robot-assisted Minimally Invasive Surgery (RMIS) pass through small incisions into the patient's body and interact with soft internal organs. The performance of traditional robotic manipulators such as the da Vinci Robotic System is limited due to insufficient flexibility of the manipulator and lack of haptic feedback. Modern surgical manipulators have taken inspiration from biology e.g. snakes or the octopus. In order for such soft and flexible arms to reconfigure itself and to control its pose with respect to organs as well as to provide haptic feedback to the surgeon, tactile sensors can be integrated with the robot's flexible structure. The work presented here takes inspiration from another area of biology: cucumber tendrils have shown to be ideal tactile sensors for the plant that they are associated with providing useful environmental information during the plant's growth. Incorporating the sensing principles of cucumber tendrils, we have created miniature sensing elements that can be distributed across the surface of soft manipulators to form a sensor network capable of acquire tactile information. Each sensing element is a retractable hemispherical tactile measuring applied pressure. The actual sensing principle chosen for each tactile makes use of optic fibres that transfer light signals modulated by the applied pressure from the sensing element to the proximal end of the robot arm. In this paper, we describe the design and structure of the sensor system, the results of an analysis using Finite Element Modeling in ABAQUS as well as sensor calibration and experimental results. Due to the simple structure of the proposed tactile sensor element, it is miniaturisable and suitable for MIS. An important contribution of this work is that the developed sensor system can be ”loosely” integrated with a soft arm effectively operating independently of the arm and without affecting the arm's motion during bending or elongation.

Internal impedance is one of the key factors determining the quality of embodied perception and action in biological organisms and robots. Though the role of impedance control in robotic actuation has been well studied, its significance in the accuracy of proprioception with embodied sensors is not well known yet. Therefore, it is important to characterize the relationship between the entropy of sensor information and the impedance of their physical embodiment, through which sensors feel the internal state of the body and the environment. In this paper, we address the role of internal impedance in the accuracy of embodied perception. To investigate this, we pose the problem of using only torque data measured at the stationary base of a two link planar manipulator, to estimate the deflection caused by an external torque in the McKibben type pivot joint with variable stiffness. Based on analytical modelling and experimental validation, this paper presents, for the first time, that non-linear static memory primitives relating internal impedance, internal kinematic variables, and forces felt at the base of the manipulator - similar to the functionality of tendon organs of biological counterparts - can be used to tune optimal internal impedance parameters to maximize the accuracy of internal state estimation during external perturbations.

The break-away friction ratio (BF-ratio), which is the ratio between friction force and the normal force at slip occurrence, is important for the prediction of incipient slip and the determination of optimal grasping forces. Conventionally, this ratio is assumed constant and approximated as the static friction coefficient. However, this ratio varies with acceleration rates and force rates applied to the grasped object and the object material, which lead to difficulties in determining optimal grasping forces that avoid slip. In this paper, we propose a novel approach based on the interactive forces to allow a robotic hand to predict object slip before its occurrence. The approach only requires the robotic hand to have a short haptic surface exploration over the object surface before manipulating it. Then, the frictional properties of the finger-object contact can be efficiently identified, and the BF-ratio can be real-time predicted to predict slip occurrence under dynamic grasping conditions. Using the predicted BF-ratio as a slip, threshold is demonstrated to be more accurate than using the static/Coulomb friction coefficient. The presented approach has been experimentally evaluated on different object surfaces, showing good performance in terms of prediction accuracy, robustness, and computational efficiency.

Advanced tactile tools in minimally invasive surgery have become a pressing need in order to reduce time and improve accuracy in localizing potential tissue abnormalities. In this regard, one of the main challenges is to be able to estimate tissue parameters in real time. In palpation, tactile information felt at a given location is identified by the viscoelastic dynamics of the neighboring tissue. Due to this reason the tissue examination behavior and the distribution of viscoelastic parameters in tissue should be considered in conjunction. This paper investigates the salient features of palpation behavior on soft tissue determining the effectiveness of localizing hard nodules. Experimental studies involving human participants, and validation tests using finite element simulations and a tele-manipulator, were carried out. Two distinctive tissue examination strategies in force-velocity modulation for the given properties of target tissue were found. Experimental results suggest that force-velocity modulations during continuous path measurements are playing an important role in the process of mechanical soft tissue examination. These behavioral insights, validated by detailed numerical models and robotic experimentations shed light on future designs of optimal robotic palpation.

Robot programming by demonstration encompasses a wide range of learning strategies, from simple mimicking of the demonstrator's actions to the higher level extraction of the underlying intent. By focusing on this last form, we study the problem of extracting the reward function explaining the demonstrations from a set of candidate reward functions, and using this information for self-refinement of the skill. This definition of the problem has links with inverse reinforcement learning problems in which the robot autonomously extracts an optimal reward function that defines the goal of the task. By relying on Gaussian mixture models, the proposed approach learns how the different candidate reward functions are combined, and in which contexts or phases of the task they are relevant for explaining the user's demonstrations. The extracted reward profile is then exploited to improve the skill with a self-refinement approach based on expectation-maximization, allowing the imitator to reach a skill level that goes beyond the demonstrations. The approach can be used to reproduce a skill in different ways or to transfer tasks across robots of different structures. The proposed approach is tested in simulation with a new type of continuum robot (STIFF-FLOP), using kinesthetic demonstrations from a Barrett WAM manipulator.

This paper investigates how a walker could estimate the variability of an arbitrary set of state variables when migrating on visco-elastic grounds. The state variables are a function of both the visco-elastic settings of the walking body and soft terrain parameters. A rimless wheel model was developed using a Lagrangian approach in order to obtain analytical solutions for migration across ground conditions. An algorithm was then developed to determine the steady value of the variables as a function of the difference in ground and hub parameters involved in the migration. A generalised migration metaparameter, Δg, function of this difference, was then extrapolated using polynomial approximation. Δgcan be used to estimate the expected variability at a state given information on actual and previous ground parameters. A second parameter, Δh, describing local variability of a given state on a given terrain, is used to generate a predictive algorithm capable of stabilising the rimless wheel setup when subject to an abrupt change in ground parameters. We actuate the rimless wheel with a constant torque leaving it to develop any speed profile for a given visco-elastic impedance distribution of the ground and its own vertical visco-elastic impedance. The ground is altered depending on the two migration meta-parameters (Δgand Δr), ensuring both local and migration stability.

In the case of human intervention in disaster response operations like indoor firefighting, where the environment perception is limited due to thick smoke, noise in the oxygen masks and clutter, not only limit the environmental perception of the human responders, but also causes distress. An intelligent agent (man/machine) with full environment perceptual capabilities is an alternative to enhance navigation in such unfavorable environments. Since haptic communication is the least affected mode of communication in such cases, we consider human demonstrations to use a hard rein to guide blindfolded followers with auditory distraction to be a good paradigm to extract salient features of guiding using hard reins. Based on numerical simulations and experimental systems identification based on demonstrations from eight pairs of human subjects, we show that, the relationship between the orientation difference between the follower and the guider, and the lateral swing patterns of the hard rein by the guider can be explained by a novel 3rdorder auto regressive predictive controller. Moreover, by modeling the two party voluntary movement dynamics using a virtual damped inertial model, we were able to model the mutual trust between two parties. In the future, the novel controller extracted based on human demonstrations can be tested on a human-robot interaction scenario to guide a visually impaired person in various applications like fire fighting, search and rescue, medical surgery, etc.

Robot-assisted minimal invasive surgery made it possible to improve the quality of surgical procedures and to enhance clinical outcomes. However, the need to palpate soft tissue organs with the aim to localize potential sites of abnormalities in real time has been recognized. For this work, ten subjects were recruited to perform a remote palpation procedure on a silicone phantom utilizing a tele-manipulation setup, to study their behavior when remotely palpating soft tissue. The stiffness values acquired during the remote palpation of a silicone phantom were transferred to the subjects by means of haptic and visual feedback. Participating subjects were asked to detect hard nodules in the silicone tissue using two distinct strategies: a) randomly chosen movements, and b) trajectory pattern, based on manual palpation techniques for clinical breast examination. We have compared relevant parameters, defining patterns observed during manual palpation, with the counterpart patterns occurring during remote palpation. The results show the effectiveness of applying palpation trajectory pattern used during manual soft tissue examination to tele-manipulation palpation.

During open surgery, surgeons can perceive the locations of tumors inside soft-tissue organs using their fingers. Palpating an organ, surgeons acquire distributed pressure (tactile) information that can be interpreted as stiffness distribution across the organ -an important aid in detecting buried tumors in otherwise healthy tissue. Previous research has focused on haptic systems to feedback the tactile sensation experienced during palpation to the surgeon during minimally invasive. However, the control complexity and high cost of tactile actuators limits its current application. This paper describes a pneumatic multi-fingered haptic feedback system for robot-assisted minimally invasive surgery. It simulates soft tissue stiffness by changing the pressure of an air balloon and recreates the deformation of fingers as experienced during palpation. The pneumatic haptic feedback actuator is validated by using finite element analysis. The results prove that the interaction stress between the fingertip and the soft tissue as well as the deformation of fingertips during palpation can be recreated by using our pneumatic multi-fingered haptic feedback method.

The aim of this paper is to improve the optimality and accuracy of techniques to guide a human in limited visibility and auditory conditions such as in fire-fighting in warehouses or similar environments. At present, breathing apparatus (BA) wearing fire-fighters move in teams following walls. Due to limited visibility and high noise in the oxygen masks, they predominantly depend on haptic communication through reins. An intelligent agent (man/machine) with full environment perceptual capabilities is an alternative to enhance navigation in such unfavorable environments, just like a dog guiding a blind person. This paper proposes an optimal state-dependent control policy to guide a follower with limited environmental perception, by an intelligent and environmentally perceptive agent. Based on experimental systems identification and numerical simulations on human demonstrations from eight pairs of participants, we show that the guiding agent and the follower experience learning for a optimal stable state-dependent novel 3rd and 2nd order auto regressive predictive and reactive control policies respectively. Our findings provide a novel theoretical basis to design advanced human-robot interaction algorithms in a variety of cases that require the assistance of a robot to perceive the environment by a human counterpart.

This paper proposes a new computationally fast algorithm for classifying the primitive shape and pose of the local contact area in real-time using a tactile array sensor attached on a robotic fingertip. The proposed approach abstracts the lower structural property of the tactile image by analyzing the covariance between pressure values and their locations on the sensor and identifies three orthogonal principal axes of the pressure distribution. Classifying contact shapes based on the principal axes allows the results to be invariant to the rotation of the contact shape. A naïve Bayes classifier is implemented to classify the shape and pose of the local contact shapes. Using an off-shelf low resolution tactile array sensor which comprises of 5×9 pressure elements, an overall accuracy of 97.5% has been achieved in classifying six primitive contact shapes. The proposed method is very computational efficient (total classifying time for a local contact shape = 576μs (1736 Hz)). The test results demonstrate that the proposed method is practical to be implemented on robotic hands equipped with tactile array sensors for conducting manipulation tasks where real-time classification is essential.

This paper investigates possible sources of variability in the dynamics of legged locomotion, even in its most idealized form. The rimless wheel model is a seemingly deterministic legged dynamic system, popular within the legged locomotion community for understanding basic collision dynamics and energetics during passive phases of walking. Despite the simplicity of this legged model, however, experimental motion capture data recording the passive step-to-step dynamics of a rimless wheel down a constant-slope terrain actually demonstrate significant variability, providing strong evidence that stochasticity is an intrinsic-and thus unavoidable-property of legged locomotion that should be modeled with care when designing reliable walking machines. We present numerical comparisons of several hypotheses as to the dominant source(s) of this variability: 1) the initial distribution of the angular velocity, 2) the uneven profile of the leg lengths and 3) the distribution of the coefficients of friction and restitution across collisions. Our analysis shows that the 3rd hypothesis most accurately predicts the noise characteristics observed in our experimental data while the 1st hypothesis is also valid for certain contexts of terrain friction. These findings suggest that variability due to ground contact dynamics, and not simply due to geometric variations more typically modeled in terrain, is important in determining the stochasticity and resulting stability of walking robots. Although such ground contact variability might be an expected result in field robotics on significantly rough terrain, we again note our experimental data applies seemingly deterministic-looking terrains: our results suggest that stochastic ground collision models should play an important role in the analysis and optimization of dynamic performance and stability in robot walking.

This paper investigates how a walker could maintain the variability of an arbitrary set of state variables within desired margins while walking on an uncertain soft terrain. The state variables are dynamically related to the visco-elastic impedance parameters of the body on a given set of uncertain soft terrains using internal memory primitives. A rimless wheel, a walker in its simplest form, is used to perform numerical simulations based on analytical dynamic models and hardware experiments to test a novel algorithm. The rimless wheel model is widely used by the legged locomotion research community to understand basic collision and energetics during passive dynamic walking. Very often, variability of punctuated force perturbations across collisions between the legs and the ground cause uncertain steady state dynamics of walking. This leads to the existence of a finite probability that certain state variables can reach unstable regions. Such phenomenon is known as metastability of walking. In this case, we actuate the rimless wheel with a constant torque leaving it to develop any speed profile for a given visco-elastic impedance distribution of the ground and its own vertical visco-elastic impedance that pushes the rimless wheel against the ground. Here we measure the robustness of the novel algorithm by its ability to shift the distribution of collision forces to a safer region in order to minimize the probability of reaching a given critical force threshold. Our analysis shows that the generalization of the variability of walking in different regions of the internal and external visco-elastic impedance spaces can simplify the computational challenges of robust walking on uncertain visco-elastic terrains.

This paper presents the kinematics, dynamics, and experimental results for a novel quadruped robot using continuum limbs. We propose soft continuum limbs as a new paradigm for robotic locomotion in unstructured environments due to their potential to generate a wide array of locomotion behaviors ranging from walking, trotting, crawling, and propelling to whole arm grasping as a means of negotiating difficult obstacles. A straightforward method to derive the kinematics and dynamics for the proposed quadruped has been demonstrated through numerical simulations. Initial experiments on a prototype continuum quadruped demonstrate the ability to stand up from a flat-belly stance, absorb external disturbances such as maintaining stability after dropping from a height and after being perturbed by a collision, and crawling on flat and cluttered environments. Experiment results provide evidence that locomotion with soft continuum limbs are feasible and usable in unstructured environments for variety of applications.

Maintaining the grip on an artery with a pulsating impedance, holding the steering wheel of a vehicle on a bumpy terrain, or holding a live hamster without excessive squeezing may be trivial tasks to most humans. However, a robot will find it very difficult to maintain the grip of such uncertain objects based on real-time feedback control. This paper presents a stochastic control law to maintain the grip on an uncertain object while manipulating against external forces. The radial impedance parameters of the soft object is assumed to undergo Gaussian random variations. Here we demonstrate that the proposed model free grip controller can maintain a safe grip at two diagonally opposite points of the object merely based on the statistics of the normal force. It accomplishes this by computing a probability of grip failure to adapt the compression on the soft object. A novel optimal estimation algorithm that can concurrently estimate the unknown impedance parameters of the object and the states of the coupled dynamic system is discussed as a potential tool to be used in predictive optimal impedance control on uncertain objects. Experimental results on adaptive grip control on a cylindrical tube inflated and deflated with a Gaussian random variation has been presented to validate the algorithm.

Robotic manipulators for minimally invasive surgeries have traditionally been rigid, with a steerable end effector. While the rigidity of manipulators improve precision and controllability, it limits reachability and dexterity in constrained environments. Soft manipulators with controllable stiffness on the other hand, can be deployed in single port or natural orifice surgical applications to reach a wide range of areas inside the body, while being able to passively adapt to uncertain external forces, adapt the stiffness distribution to suit the kinematic and dynamic requirements of the task, and provide flexibility for configuration control. Here, we present the design of a snake-like laboratory made soft robot manipulator of 20 mm in average diameter, which can actuate, soften, or stiffen joints independently along the length of the manipulator by combining granular jamming with McKibben actuators. It presents a comprehensive study on the relative contributions of the granule size, material type, and membrane coupling on the range, profile, and variability of stiffness.

The growing demand for technological innovation to enable empowerment of developing communities requires new and creative educational initiatives. Thus, well designed higher educational initiatives geared towards appropriate technology for developing communities can have a significant global impact. This paper presents the challenges and benefits of three higher education initiatives in Sri Lanka, Ghana, and the USA that focus on innovating and implementing relevant technology for developing communities. The authors examine the potential intersections of robotics and its component technologies with education and sustainable development. The paper concludes with an assessment of factors that contribute to the success of higher educational initiatives designed to enable technology relevant to developing communities.