S. Shimoda

Bio: S. Shimoda is an academic researcher from Toyota. The author has contributed to research in topics: Artificial neural network & Humanoid robot. The author has an hindex of 1, co-authored 1 publications receiving 24 citations.

Adaptability of Tacit Learning in Bipedal Locomotion

Abstract: The capability of adapting to unknown environmental situations is one of the most salient features of biological regulations. This capability is ascribed to the learning mechanisms of biological regulatory systems that are totally different from the current artificial machine-learning paradigm. We consider that all computations in biological regulatory systems result from the spatial and temporal integration of simple and homogeneous computational media such as the activities of neurons in brain and protein-protein interactions in intracellular regulations. Adaptation is the outcome of the local activities of the distributed computational media. To investigate the learning mechanism behind this computational scheme, we proposed a learning method that embodies the features of biological systems, termed tacit learning. In this paper, we elaborate this notion further and applied it to bipedal locomotion of a 36DOF humanoid robot in order to discuss the adaptation capability of tacit learning comparing with that of conventional control architectures and that of human beings. Experiments on walking revealed a remarkably high adaptation capability of tacit learning in terms of gait generation, power consumption and robustness.

A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control

Abstract: This paper proposes a novel brain-machine interfacing (BMI) paradigm for control of a multijoint redundant robot system. Here, the user would determine the direction of end-point movement of a 3-degrees of freedom (DOF) robot arm using motor imagery electroencephalography signal with co-adaptive decoder (adaptivity between the user and the decoder) while a synergetic motor learning algorithm manages a peripheral redundancy in multi-DOF joints toward energy optimality through tacit learning. As in human motor control, torque control paradigm is employed for a robot to be adaptive to the given physical environment. The dynamic condition of the robot arm is taken into consideration by the learning algorithm. Thus, the user needs to only think about the end-point movement of the robot arm, which allows simultaneous multijoints control by BMI. The support vector machine-based decoder designed in this paper is adaptive to the changing mental state of the user. Online experiments reveals that the users successfully reach their targets with an average decoder accuracy of over 75% in different end-point load conditions.

Muscle synergy space: learning model to create an optimal muscle synergy

Abstract: Muscle redundancy allows the central nervous system (CNS) to choose a suitable combination of muscles from a number of options. This flexibility in muscle combinations allows for efficient behaviors to be generated in daily life. The computational mechanism of choosing muscle combinations, however, remains a long-standing challenge. One effective method of choosing muscle combinations is to create a set containing the muscle combinations of only efficient behaviors, and then to choose combinations from that set. The notion of muscle synergy, which was introduced to divide muscle activations into a lower-dimensional synergy space and time-dependent variables, is a suitable tool relevant to the discussion of this issue. The synergy space defines the suitable combinations of muscles, and time-dependent variables vary in lower-dimensional space to control behaviors. In this study, we investigated the mechanism the CNS may use to define the appropriate region and size of the synergy space when performing skilled behavior. Two indices were introduced in this study, one is the synergy stability index (SSI) that indicates the region of the synergy space, the other is the synergy coordination index (SCI) that indicates the size of the synergy space. The results on automatic posture response experiments show that SSI and SCI are positively correlated with the balance skill of the participants, and they are tunable by behavior training. These results suggest that the CNS has the ability to create optimal sets of efficient behaviors by optimizing the size of the synergy space at the appropriate region through interacting with the environment.

Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning.

Abstract: A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works. Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system. In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics. We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques. We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions. Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is minimized down to 12% for no load at hand, 16% for a 0.5kg load condition. The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach.

Synergetic Learning Control Paradigm for Redundant Robot to Enhance Error-Energy Index

Abstract: In order to perform energetically efficient motion as in human control, so-called optimization-based approach is commonly used in both robotics and neuroscience. Such an optimization approach can provide optimal solution when the prior dynamics information of the manipulator and the environment is explicitly given. However, the environment, where the robot faces with in a real world rarely has such a situation. The dynamics conditions change by the contact situation or the hand load for the manipulation task. Simple computational paradigm to realize both adaptability and learning is essential to bridge the gap between learning and control process in redundancy. We verify a novel synergetic learning control paradigm in reaching task of redundant manipulator. The performance in handling different dynamics conditions is evaluated in dual criteria of error-energy (E-E) coupling without prior knowledge of the given environmental dynamics and with model-optimization-free approach. This paper aims at investigating the ability of phenomenological optimization with the proposed human-inspired learning control paradigm for environmental dynamics recognition and adaptation, which is different from conventional model optimization approach. E-E index is introduced to evaluate not only the tracking performance, but also the error reduction rate per the energy consumption.

Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation.

Abstract: Background For mechanically reconstructing human biomechanical function, intuitive proportional control and robustness to unexpected situations are required. Particularly, creating a functional hand prosthesis is a typical challenge in the reconstruction of lost biomechanical function. Nevertheless, currently available control algorithms are in the development phase. The most advanced algorithms for controlling multifunctional prosthesis are machine learning and pattern recognition of myoelectric signals. Despite the increase in computational speed, these methods cannot avoid the requirement of user consciousness and classified separation errors. “Tacit Learning System” is a simple but novel adaptive control strategy that can self-adapt its posture to environment changes. We introduced the strategy in the prosthesis rotation control to achieve compensatory reduction, as well as evaluated the system and its effects on the user. Methods We conducted a non-randomized study involving eight prosthesis users to perform a bar relocation task with/without Tacit Learning System support. Hand piece and body motions were recorded continuously with goniometers, videos, and a motion-capture system. Findings Reduction in the participants’ upper extremity rotatory compensation motion was monitored during the relocation task in all participants. The estimated profile of total body energy consumption improved in five out of six participants. Interpretation Our system rapidly accomplished nearly natural motion without unexpected errors. The Tacit Learning System not only adapts human motions but also enhances the human ability to adapt to the system quickly, while the system amplifies compensation generated by the residual limb. The concept can be extended to various situations for reconstructing lost functions that can be compensated.