Visual, Motor and Attentional Influences on Proprioceptive Contributions to Perception of Hand Path Rectilinearity during Reaching

We examined how proprioceptive contributions to perception of hand path straightness are influenced by visual, motor and attentional sources of performance variability during horizontal planar reaching. Subjects held the handle of a robot that constrained goal-directed movements of the hand to the paths of controlled curvature. Subjects attempted to detect the presence of hand path curvature during both active (subject driven) and passive (robot driven) movements that either required active muscle force production or not. Subjects were less able to discriminate curved from straight paths when actively reaching for a target versus when the robot moved their hand through the same curved paths. This effect was especially evident during robot-driven movements requiring concurrent activation of lengthening but not shortening muscles. Subjects were less likely to report curvature and were more variable in reporting when movements appeared straight in a novel “visual channel” condition previously shown to block adaptive updating of motor commands in response to deviations from a straight-line hand path. Similarly, compromised performance was obtained when subjects simultaneously performed a distracting secondary task (key pressing with the contralateral hand). The effects compounded when these last two treatments were combined. It is concluded that environmental, intrinsic and attentional factors all impact the ability to detect deviations from a rectilinear hand path during goal-directed movement by decreasing proprioceptive contributions to limb state estimation. In contrast, response variability increased only in experimental conditions thought to impose additional attentional demands on the observer. Implications of these results for perception and other sensorimotor behaviors are discussed

Bayesian Nonparametric Learning of Cloth Models for Real-time State Estimation

Robotic solutions to clothing assistance can significantly improve quality of life for the elderly and disabled. Real-time estimation of the human-cloth relationship is crucial for efficient learning of motor skills for robotic clothing assistance. The major challenge involved is cloth-state estimation due to inherent nonrigidity and occlusion. In this study, we present a novel framework for real-time estimation of the cloth state using a low-cost depth sensor, making it suitable for a feasible social implementation. The framework relies on the hypothesis that clothing articles are constrained to a low-dimensional latent manifold during clothing tasks. We propose the use of manifold relevance determination (MRD) to learn an offline cloth model that can be used to perform informed cloth-state estimation in real time. The cloth model is trained using observations from a motion capture system and depth sensor. MRD provides a principled probabilistic framework for inferring the accurate motion-capture state when only the noisy depth sensor feature state is available in real time. The experimental results demonstrate that our framework is capable of learning consistent task-specific latent features using few data samples and has the ability to generalize to unseen environmental settings. We further present several factors that affect the predictive performance of the learned cloth-state model

Design, fabrication and control of soft robots

Conventionally, engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are easily modelled as rigid members connected at discrete joints. Natural systems, however, often match or exceed the performance of robotic systems with deformable bodies. Cephalopods, for example, achieve amazing feats of manipulation and locomotion without a skeleton; even vertebrates such as humans achieve dynamic gaits by storing elastic energy in their compliant bones and soft tissues. Inspired by nature, engineers have begun to explore the design and control of soft-bodied robots composed of compliant materials. This Review discusses recent developments in the emerging field of soft robotics.National Science Foundation (U.S.) (Grant IIS-1226883

Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

Muscle activation mapping of skeletal hand motion: an evolutionary approach.

Creating controlled dynamic character animation consists of mathe- matical modelling of muscles and solving the activation dynamics that form the key to coordination. But biomechanical simulation and control is com- putationally expensive involving complex di erential equations and is not suitable for real-time platforms like games. Performing such computations at every time-step reduces frame rate. Modern games use generic soft- ware packages called physics engines to perform a wide variety of in-game physical e ects. The physics engines are optimized for gaming platforms. Therefore, a physics engine compatible model of anatomical muscles and an alternative control architecture is essential to create biomechanical charac- ters in games. This thesis presents a system that generates muscle activations from captured motion by borrowing principles from biomechanics and neural con- trol. A generic physics engine compliant muscle model primitive is also de- veloped. The muscle model primitive forms the motion actuator and is an integral part of the physical model used in the simulation. This thesis investigates a stochastic solution to create a controller that mimics the neural control system employed in the human body. The control system uses evolutionary neural networks that evolve its weights using genetic algorithms. Examples and guidance often act as templates in muscle training during all stages of human life. Similarly, the neural con- troller attempts to learn muscle coordination through input motion samples. The thesis also explores the objective functions developed that aids in the genetic evolution of the neural network. Character interaction with the game world is still a pre-animated behaviour in most current games. Physically-based procedural hand ani- mation is a step towards autonomous interaction of game characters with the game world. The neural controller and the muscle primitive developed are used to animate a dynamic model of a human hand within a real-time physics engine environment

Patient-specific simulation for autonomous surgery

An Autonomous Robotic Surgical System (ARSS) has to interact with the complex anatomical environment, which is deforming and whose properties are often uncertain. Within this context, an ARSS can benefit from the availability of patient-specific simulation of the anatomy. For example, simulation can provide a safe and controlled environment for the design, test and validation of the autonomous capabilities. Moreover, it can be used to generate large amounts of patient-specific data that can be exploited to learn models and/or tasks. The aim of this Thesis is to investigate the different ways in which simulation can support an ARSS and to propose solutions to favor its employability in robotic surgery. We first address all the phases needed to create such a simulation, from model choice in the pre-operative phase based on the available knowledge to its intra-operative update to compensate for inaccurate parametrization. We propose to rely on deep neural networks trained with synthetic data both to generate a patient-specific model and to design a strategy to update model parametrization starting directly from intra-operative sensor data. Afterwards, we test how simulation can assist the ARSS, both for task learning and during task execution. We show that simulation can be used to efficiently train approaches that require multiple interactions with the environment, compensating for the riskiness to acquire data from real surgical robotic systems. Finally, we propose a modular framework for autonomous surgery that includes deliberative functions to handle real anatomical environments with uncertain parameters. The integration of a personalized simulation proves fundamental both for optimal task planning and to enhance and monitor real execution. The contributions presented in this Thesis have the potential to introduce significant step changes in the development and actual performance of autonomous robotic surgical systems, making them closer to applicability to real clinical conditions

Predictive Dynamic Simulation of Healthy Sit-to-Stand Movement

This thesis situates itself at the intersection of biomedical modelling and predictive simulation to synthesize healthy human sit-to-stand movement. While the importance of sit-to-stand to physical and social well-being is known, the reasons for why and how people come to perform sit-to-stand the way we do is largely unknown. This thesis establishes the determinants of sit-to-stand in healthy people so that future researchers may investigate the effects of compromised health on sit-to-stand and then explore means of intervening to preserve and restore this motion. Previous researchers have predicted how a person rises from seated. However aspects of their models, most commonly contact and muscle models, are biomechanically inconsistent and restrict their application. These researchers also have not validated their prediction results. To address these limitations and further the study of sit-to-stand prediction, the underlying themes of this thesis are in biomechanical modelling, predictive simulation, and validation. The goal of predicting sit-to-stand inspired the creation of three new models: a model of biomechanics, a model of motion, and performance criteria as a model of preference. First, the human is represented as three rigid links in the sagittal plane. As buttocks are kinetically important to sit-to-stand, a new constitutive model of buttocks is made from experimental force-deformation data. Ten muscles responsible for flexion and extension of the hips, knees, and ankles are defined in the model. Second, candidate sit-to-stand trajectories are described geometrically by a set of Bézier curves, for the first time. Third, with the assumption that healthy people naturally prioritize mechanical efficiency, disinclination to a motion is described as a cost function of joint torques, muscle stresses, and physical infeasibility including slipping and falling. This new dynamic optimization routine allows for motions of gradually increasing complexity, by adding control points to the Bézier curves, while the model's performance is improving. By comparing the predictive simulation results to normative sit-to-stand as described in the literature, for the first time, it is possible to say that the use of these models and optimal control strategy together has produced motions characteristic of healthy sit-to-stand. This work bridges the gap between predictive simulation results and experimental human results and in doing so establishes a benchmark in sit-to-stand prediction. In predicting healthy sit-to-stand, it makes a necessary step toward predicting pathological sit-to-stand, and then to predicting the results of intervention to inform medical design and planning