On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

A physical model suggests that hip-localized balance sense in birds improves state estimation in perching: implications for bipedal robots

In addition to a vestibular system, birds uniquely have a balance-sensing organ within the pelvis, called the lumbosacral organ (LSO). The LSO is well developed in terrestrial birds, possibly to facilitate balance control in perching and terrestrial locomotion. No previous studies have quantified the functional benefits of the LSO for balance. We suggest two main benefits of hip-localized balance sense: reduced sensorimotor delay and improved estimation of foot-ground acceleration. We used system identification to test the hypothesis that hip-localized balance sense improves estimates of foot acceleration compared to a head-localized sense, due to closer proximity to the feet. We built a physical model of a standing guinea fowl perched on a platform, and used 3D accelerometers at the hip and head to replicate balance sense by the LSO and vestibular systems. The horizontal platform was attached to the end effector of a 6 DOF robotic arm, allowing us to apply perturbations to the platform analogous to motions of a compliant branch. We also compared state estimation between models with low and high neck stiffness. Cross-correlations revealed that foot-to-hip sensing delays were shorter than foot-to-head, as expected. We used multi-variable output error state-space (MOESP) system identification to estimate foot-ground acceleration as a function of hip- and head-localized sensing, individually and combined. Hip-localized sensors alone provided the best state estimates, which were not improved when fused with head-localized sensors. However, estimates from head-localized sensors improved with higher neck stiffness. Our findings support the hypothesis that hip-localized balance sense improves the speed and accuracy of foot state estimation compared to head-localized sense. The findings also suggest a role of neck muscles for active sensing for balance control: increased neck stiffness through muscle co-contraction can improve the utility of vestibular signals. Our engineering approach provides, to our knowledge, the first quantitative evidence for functional benefits of the LSO balance sense in birds. The findings support notions of control modularity in birds, with preferential vestibular sense for head stability and gaze, and LSO for body balance control,respectively. The findings also suggest advantages for distributed and active sensing for agile locomotion in compliant bipedal robots

On neuromechanical approaches for the study of biological and robotic grasp and manipulation

abstract: Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank and open-minded assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas at the interface of neuromechanics, neuroscience, rehabilitation and robotics.The electronic version of this article is the complete one and can be found online at: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-017-0305-

Doctor of Philosophy

dissertationModeling the human hand's tendon system can bring better understanding to roboticists trying to create tendon based robotic hands and clinicians trying to identify new surgical solutions to hand tendon injuries. Accurate modeling of the hand's tendon system is complex due to the intricate nature of how tendons route and attach to each other and the skeleton system. These tendon complexities have restricted previous tendon models to single finger models with limited anatomical accuracy and no ability to depict fingertip contact force with external surfaces. This dissertation outlines the use of bond graph modeling to create and improve upon previous tendon models of the single finger. This bond graph tendon model of the single finger is the first model to incorporate many anatomical features, including tendon interconnections and anatomical stiffness, of the tendon system. A graphical user interface is presented to visually explore the relationship between tendon input and finger posture. The bond graph tendon model is validated using cadaver and in vivo experiments, along with the Anatomically Correct Testbed (ACT) Hand, which is a biologically inspired robotic hand that accurately mimics the bone structure, joints, and tendons of the human hand. Comparisons of the bond graph tendon model to in vivo data on finger joint coupling and fingertip pinch force, and cadaver data on the tendon system showed strong correlation in trends and magnitudes. A motion experiment, comparing the joint angle results of tendon excursions of the bond graph tendon model and the ACT Hand, and a force experiment, comparing the fingertip force generation of the two systems, were devised to validate the bond graph tendon model. The results of the motion experiments showed close agreement between the two systems (< 8 joint angle error), while the results of the force experiments showed a larger range correlation between the two systems (8-42% difference). The result of the validation experiments showed that the bond graph tendon model is able to accurately represent the tendon system of the finger. The model is also the first tendon model to allow for exploration of the effects of fingertip contact on the tendon system

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

What is morphological computation? On how the body contributes to cognition and control

The contribution of the body to cognition and control in natural and artificial agents is increasingly described as “off-loading computation from the brain to the body”, where the body is said to perform “morphological computation”. Our investigation of four characteristic cases of morphological computation in animals and robots shows that the ‘off-loading’ perspective is misleading. Actually, the contribution of body morphology to cognition and control is rarely computational, in any useful sense of the word. We thus distinguish (1) morphology that facilitates control, (2) morphology that facilitates perception and the rare cases of (3) morphological computation proper, such as ‘reservoir computing.’ where the body is actually used for computation. This result contributes to the understanding of the relation between embodiment and computation: The question for robot design and cognitive science is not whether computation is offloaded to the body, but to what extent the body facilitates cognition and control – how it contributes to the overall ‘orchestration’ of intelligent behavior

Cortical Orchestra Conducted by Purpose and Function

학위논문(박사)--서울대학교 대학원 :자연과학대학 협동과정 뇌과학전공,2020. 2. 정천기.촉각과 자기수용감각은 우리의 생존 및 일상생활에 절대적인 영향을 미치는 중요한 감각 기능이다. 말초신경계에서 이 두 가지 기능들에 필요한 정보를 수집하고 전달하는 기계적 수용기 및 그 구심성 신경들에 대한 신호 전달 메커니즘 및 그 특징들은 상대적으로 잘 알려져 있는 편이다. 그러나, 촉각과 자기수용감각을 형성하기 위한 인간 뇌의 피질에서의 정보 처리 메커니즘에 대하여 우리가 현재 알고 있는 바는 극히 일부분이다. 이 논문에서 제시하는 일련의 연구들은 인간 뇌 피질 단계에서 촉각과 자기수용감각의 지각적 처리과정에 대한 거시적 신경계 정보처리 메커니즘을 다룬다. 첫 번째 연구에서는 뇌피질뇌파를 이용하여 인간 일차 및 이차 체성감각 피질에서 인공적인 자극과 일상생활에서 접할 수 있는 자극을 포함하는 다양한 진동촉감각 및 질감 자극에 대한 거시적 신경계 정보처리 특성을 밝혔다. 이 연구에서는 일차 및 이차 체성감각 피질의 촉감각 주파수 특이적인 하이-감마 영역 신경활동이 자극 주파수에 따라 각각 상이한 시간적 다이나믹스를 가지고 변화하는 것을 확인하였다. 또한, 이러한 하이-감마 활동은 성긴 질감과 미세한 입자감을 가진 자연스러운 질감 자극에 대해서도 진동촉감각의 경우와 유사한 패턴을 보였다. 이러한 결과들은 인간의 진동촉감각이 매우 단순한 형태에 자극일지라도 대뇌 체성감각 시스템에 있어 거시적인 다중 영역에서의 계층적 정보처리를 동반한다는 점을 시사한다. 두 번째 연구에서는 인간의 움직임과 관련된 두정엽 영역에서의 하이-감마 뇌활성이 자기수용감각과 같은 말초신경계로부터의 체성감각 피드백을 주로 반영하는지, 아니면 움직임 준비 및 제어를 위한 피질 간 신경 프로세스에 대한 활동을 반영하는지를 조사하였다. 연구 결과, 자발적 운동 중 대뇌 운동감각령에서의 하이-감마 활동은 일차 체성감각피질이 일차 운동피질보다 더 지배적인 것으로 나타났다. 또한 이 연구에서는, 움직임과 관련된 일차 체성감각피질에서의 하이-감마 뇌활동은 말초신경계로부터의 자기수용감각과 촉각에 대한 신경계 정보처리를 주로 반영하는 것을 밝혔다. 이러한 연구들을 바탕으로, 마지막 연구에서는 인간 대뇌에서의 체성감각 지각 프로세스에 대한 거시적 피질 간 네트워크를 규명하고자 하였다. 이를 위해, 51명의 뇌전증 환자에게서 체성감각을 유발했던 뇌피질전기자극 데이터와 46명의 환자에게서 촉감각 자극 및 운동 수행 중에 측정한 뇌피질뇌파 하이-감마 매핑 데이터를 종합적으로 분석하였다. 그 결과, 체성감각 지각 프로세스는 대뇌에서 넓은 영역에 걸쳐 분포하는 체성감각 관련 네트워크의 신경 활성을 수반한다는 것을 알아냈다. 또한, 뇌피질전기자극을 통한 대뇌 지도와 하이-감마 매핑을 통한 대뇌 지도는 서로 상당한 유사성을 보였다. 흥미롭게도, 뇌피질전기자극과 하이-감마 활동을 종합한 뇌지도들로부터 체성감각 관련 뇌 영역의 공간적 분포가 체성감각 기능에 따라 서로 달랐고, 그에 해당하는 각 영역들은 서로 뚜렷하게 다른 시간적 다이나믹스를 가지고 순차적으로 활성화되었다. 이러한 결과들은 체성감각에 대한 거시적 신경계 프로세스가 그 지각적 기능에 따라 뚜렷이 다른 계층적 네트워크를 가진다는 점을 시사한다. 더 나아가, 본 연구에서의 결과들은 체성감각 시스템의 지각-행동 관련 신경활동 흐름에 관한 이론적인 가설에 대하여 설득력 있는 증거를 제시하고 있다.Tactile and proprioceptive perceptions are crucial for our daily life as well as survival. At the peripheral level, the transduction mechanisms and characteristics of mechanoreceptive afferents containing information required for these functions, have been well identified. However, our knowledge about the cortical processing mechanism for them in human is limited. The present series of studies addressed the macroscopic neural mechanism for perceptual processing of tactile and proprioceptive perception in human cortex. In the first study, I investigated the macroscopic neural characteristics for various vibrotactile and texture stimuli including artificial and naturalistic ones in human primary and secondary somatosensory cortices (S1 and S2, respectively) using electrocorticography (ECoG). I found robust tactile frequency-specific high-gamma (HG, 50–140 Hz) activities in both S1 and S2 with different temporal dynamics depending on the stimulus frequency. Furthermore, similar HG patterns of S1 and S2 were found in naturalistic stimulus conditions such as coarse/fine textures. These results suggest that human vibrotactile sensation involves macroscopic multi-regional hierarchical processing in the somatosensory system, even during the simplified stimulation. In the second study, I tested whether the movement-related HG activities in parietal region mainly represent somatosensory feedback such as proprioception from periphery or primarily indicate cortico-cortical neural processing for movement preparation and control. I found that sensorimotor HG activities are more dominant in S1 than in M1 during voluntary movement. Furthermore, the results showed that movement-related HG activities in S1 mainly represent proprioceptive and tactile feedback from periphery. Given the results of previous two studies, the final study aimed to identify the large-scale cortical networks for perceptual processing in human. To do this, I combined direct cortical stimulation (DCS) data for eliciting somatosensation and ECoG HG band (50 to 150 Hz) mapping data during tactile stimulation and movement tasks, from 51 (for DCS mapping) and 46 patients (for HG mapping) with intractable epilepsy. The results showed that somatosensory perceptual processing involves neural activation of widespread somatosensory-related network in the cortex. In addition, the spatial distributions of DCS and HG functional maps showed considerable similarity in spatial distribution between high-gamma and DCS functional maps. Interestingly, the DCS-HG combined maps showed distinct spatial distributions depending on the somatosensory functions, and each area was sequentially activated with distinct temporal dynamics. These results suggest that macroscopic neural processing for somatosensation has distinct hierarchical networks depending on the perceptual functions. In addition, the results of the present study provide evidence for the perception and action related neural streams of somatosensory system. Throughout this series of studies, I suggest that macroscopic somatosensory network and structures of our brain are intrinsically organized by perceptual function and its purpose, not by somatosensory modality or submodality itself. Just as there is a purpose for human behavior, so is our brain.PART I. INTRODUCTION 1 CHAPTER 1: Somatosensory System 1 1.1. Mechanoreceptors in the Periphery 2 1.2. Somatosensory Afferent Pathways 4 1.3. Cortico-cortical Connections among Somatosensory-related Areas 7 1.4. Somatosensory-related Cortical Regions 8 CHAPTER 2: Electrocorticography 14 2.1. Intracranial Electroencephalography 14 2.2. High-Gamma Band Activity 18 CHAPTER 3: Purpose of This Study 24 PART II. EXPERIMENTAL STUDY 26 CHAPTER 4: Apparatus Design 26 4.1. Piezoelectric Vibrotactile Stimulator 26 4.2. Magnetic Vibrotactile Stimulator 29 4.3. Disc-type Texture Stimulator 33 4.4. Drum-type Texture Stimulator 36 CHAPTER 5: Vibrotactile and Texture Study 41 5.1. Introduction 42 5.2. Materials and Methods 46 5.2.1. Patients 46 5.2.2. Apparatus 47 5.2.3. Experimental Design 49 5.2.4. Data Acquisition and Preprocessing 50 5.2.5. Analysis 51 5.3. Results 54 5.3.1. Frequency-specific S1/S2 HG Activities 54 5.3.2. S1 HG Attenuation during Flutter and Vibration 62 5.3.3. Single-trial Vibration Frequency Classification 64 5.3.4. S1/S2 HG Activities during Texture Stimuli 65 5.4. Discussion 69 5.4.1. Comparison with Previous Findings 69 5.4.2. Tactile Frequency-dependent Neural Adaptation 70 5.4.3. Serial vs. Parallel Processing between S1 and S2 72 5.4.4. Conclusion of Chapter 5 73 CHAPTER 6: Somatosensory Feedback during Movement 74 6.1. Introduction 75 6.2. Materials and Methods 79 6.2.1. Subjects 79 6.2.2. Tasks 80 6.2.3. Data Acquisition and Preprocessing 82 6.2.4. S1-M1 HG Power Difference 85 6.2.5. Classification 86 6.2.6. Timing of S1 HG Activity 86 6.2.7. Correlation between HG and EMG signals 87 6.3. Results 89 6.3.1. HG Activities Are More Dominant in S1 than in M1 89 6.3.2. HG Activities in S1 Mainly Represent Somatosensory Feedback 94 6.4. Discussion 100 6.4.1. S1 HG Activity Mainly Represents Somatosensory Feedback 100 6.4.2. Further Discussion and Future Direction in BMI 102 6.4.3. Conclusion of Chapter 6 103 CHAPTER 7: Cortical Maps of Somatosensory Function 104 7.1. Introduction 106 7.2. Materials and Methods 110 7.2.1. Participants 110 7.2.2. Direct Cortical Stimulation 114 7.2.3. Classification of Verbal Feedbacks 115 7.2.4. Localization of Electrodes 115 7.2.5. Apparatus 116 7.2.6. Tasks 117 7.2.7. Data Recording and Processing 119 7.2.8. Mapping on the Brain 120 7.2.9. ROI-based Analysis 122 7.3. Results 123 7.3.1. DCS Mapping 123 7.3.2. Three and Four-dimensional HG Mapping 131 7.3.3. Neural Characteristics among Somatosensory-related Areas 144 7.4. Discussion 146 7.4.1. DCS on the Non-Primary Areas 146 7.4.2. Two Streams of Somatosensory System 148 7.4.3. Functional Role of ventral PM 151 7.4.4. Limitation and Perspective 152 7.4.5. Conclusion of Chapter 7 155 PART III. CONCLUSION 156 CHAPTER 8: Conclusion and Perspective 156 8.1. Perspective and Future Work 157 References 160 Abstract in Korean 173Docto