Adaptability of Tacit Learning in Bipedal Locomotion
TL;DR: Experiments on walking revealed a remarkably high adaptation capability of tacit learning in terms of gait generation, power consumption and robustness of a 36DOF humanoid robot compared with that of conventional control architectures and that of human beings.
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.
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26 May 2016
TL;DR: The user needs to only think about the end-point movement of the robot arm, which allows simultaneous multijoints control by BMI, and the support vector machine-based decoder designed in this paper is adaptive to the changing mental state of the user.
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.
41 citations
Cites methods from "Adaptability of Tacit Learning in B..."
...dynamic environment using a tacit learning approach [37] for a fixed period of 70 s....
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TL;DR: The 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.
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.
40 citations
Cites background from "Adaptability of Tacit Learning in B..."
...…introducing the optimal parameters of behavior control (Flash and Hogan, 1985; Uno et al., 1989; Alexander, 1997), the principles of biological controllers (Tanaka and Kimura, 2008; Shimoda and Kimura, 2010; Shimoda et al., 2013) and other methods, but this problem remains an open research topic....
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TL;DR: This study proposes a novel motor control paradigm based on tacit learning with task space feedback that could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach.
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.
27 citations
Cites background from "Adaptability of Tacit Learning in B..."
...Recently, a novel learning scheme named Tacit Learning was proposed (Shimoda and Kimura, 2010; Shimoda et al., 2013) as an unsupervised learning paradigm....
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...The experimental results demonstrated that the walking gait composed of primitive motions was well adapted to the environment in terms of walking efficiency (Shimoda et al., 2013)....
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...Previously in tacit learning for biped walking, joints were divided into kinematically specified and unspecified groups (Shimoda et al., 2013)....
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TL;DR: 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, is investigated.
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.
15 citations
Additional excerpts
...was developed [33] as an unsupervised learning paradigm....
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...ronment in terms of walking efficiency [33]....
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TL;DR: The Tacit Learning System is introduced in the prosthesis rotation control to achieve compensatory reduction, and the system enhances the human ability to adapt to the system quickly, while the system amplifies compensation generated by the residual limb.
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.
15 citations
Cites background from "Adaptability of Tacit Learning in B..."
...Signal accumulation is a key factor for “Tacit Learning” in the adaptation process and primitive behaviors composed of several reflex actions are gradually tuned into suitable behaviors for the environment (Shimoda and Kimura, 2010; Shimoda et al., 2012, 2013)....
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...The “Tacit Learning System” introduced by Shimoda has two main advantages for controlling the prosthesis compared to other control methods: learning speed and a simple, inexpensive system with intrinsic robustness (Shimoda et al., 2012, 2013)....
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References
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TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.
14,937 citations
"Adaptability of Tacit Learning in B..." refers background or methods in this paper
...The activities of VTNs are fundamentally governed by the classical McCulloch-Pitts neuron model [26] with the threshold modification rule to maintain the firing frequency in an appropriate range by their innate activities....
[...]
...The activity rule of VTNs is fundamentally governed by similar local rules with classical McCulloch-Pitts neuron model [26]....
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01 Jan 1973
TL;DR: By relying on the use of linguistic variables and fuzzy algorithms, the approach provides an approximate and yet effective means of describing the behavior of systems which are too complex or too ill-defined to admit of precise mathematical analysis.
Abstract: The approach described in this paper represents a substantive departure from the conventional quantitative techniques of system analysis. It has three main distinguishing features: 1) use of so-called ``linguistic'' variables in place of or in addition to numerical variables; 2) characterization of simple relations between variables by fuzzy conditional statements; and 3) characterization of complex relations by fuzzy algorithms. A linguistic variable is defined as a variable whose values are sentences in a natural or artificial language. Thus, if tall, not tall, very tall, very very tall, etc. are values of height, then height is a linguistic variable. Fuzzy conditional statements are expressions of the form IF A THEN B, where A and B have fuzzy meaning, e.g., IF x is small THEN y is large, where small and large are viewed as labels of fuzzy sets. A fuzzy algorithm is an ordered sequence of instructions which may contain fuzzy assignment and conditional statements, e.g., x = very small, IF x is small THEN Y is large. The execution of such instructions is governed by the compositional rule of inference and the rule of the preponderant alternative. By relying on the use of linguistic variables and fuzzy algorithms, the approach provides an approximate and yet effective means of describing the behavior of systems which are too complex or too ill-defined to admit of precise mathematical analysis.
8,547 citations
Book•
01 Jun 1991
TL;DR: A new architecture for controlling mobile robots is described, building a robust and flexible robot control system that has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms.
Abstract: A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.
7,759 citations
01 Mar 1986
TL;DR: In this paper, a new architecture for controlling mobile robots is described, which is made up of asynchronous modules that communicate over low-bandwidth channels, each module is an instance of a fairly simple computational machine.
Abstract: A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.
7,291 citations
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
Abstract: Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd the organization of behavior as the choice of reading, you can find here.
3,986 citations