We propose a trajectory planning and control theory for continuous movements such as connected cursive handwriting and continuous natural speech. Its hardware is based on our previously proposed forward-inverse-relaxation neural network (Wada & Kawato, 1993). Computationally, its optimization principle is the minimum torque(cid:173) change criterion. Regarding the representation level, hard constraints satisfied by a trajectory are represented as a set of via-points extracted from a handwritten character. Accordingly, we propose a via-point estimation algorithm that estimates via-points by repeating the trajectory formation of a character and the via-point extraction from the character.

It was an astounding request. A year ago, neuroscientist Mitsuo Kawato called on the Japanese government to commit 50 billion yen ($446 million) a year for the next three decades. The dream: an Apollo-like program to build a robot with the mental, physical, and emotional capabilities of a 5-year-old child. Kawato called his plan the Atom Project, named for the popular postwar cartoon Tetsuwan Atom (known as Astroboy in the US), the story of a superhero boy robot. This article has been reproduced in a new format and may be missing content or contain faulty links.

On a crisp october day last year, Carnegie Mellon University's Robotics Institute kicked off its 25th-anniversary celebration, as the world's robotics experts came to Pittsburgh to see C-3PO, Shakey the robot, Honda's Asimo, and Astro Boy inducted into the Robot Hall of Fame. The next day saw demonstrations of running, snaking, and bagpipe-playing bots. On the third day, it was Mitsuo Kawato's turn to speak. The lights went down, and the director of the ATR Computational Neuroscience Laboratories in Kyoto, Japan, made his way to the stage to the beat of rock music. Despite such a welcome, Kawato is an outsider here, dismissive of the self-congratulation that creeps into conversations about modern robotics.

Researchers in Japan are using a state-of-the-art technique to read and then amplify self-confidence in study participants. Dr. Mitsuo Kawato, director of the Computational Neuroscience Laboratories at the Advanced Telecommunications Research Institute International in Kyoto, Japan, has pioneered the process called "Decoded Neurofeedback." The technique used brain scanning to monitor and detect the occurrence of specific complex patterns of activity tied to high self-confidence states, while 17 participants performed a simple perceptual task. Whenever a pattern of high confidence was detected, participants received a small monetary reward. By doing this, researchers were able to directly boost a person's own confidence unconsciously, meaning the participants were unaware of the manipulation taking place.

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and oftpn uncprtain environmental conditions. This paper describes a new modular approach to human motor learning and control, baspd on multiple pairs of inverse (controller) and forward (prpdictor) models. This architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given em'ironm0nt. Simulations of object manipulation demonstrates the ability to learn mUltiple objects, appropriate generalization to novel objects and the inappropriate activation of motor programs based on visual cues, followed by online correction, seen in the "size-weight illusion".

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and oftpn uncprtain environmental conditions. This paper describes a new modular approach to human motor learning and control, baspd on multiple pairs of inverse (controller) and forward (prpdictor) models. This architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given em'ironm0nt. Simulations of object manipulation demonstrates the ability to learn mUltiple objects, appropriate generalization to novel objects and the inappropriate activation of motor programs based on visual cues, followed by online correction, seen in the "size-weight illusion".

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and oftpn uncprtain environmental conditions. This paper describes a new modular approach tohuman motor learning and control, baspd on multiple pairs of inverse (controller) and forward (prpdictor) models. This architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given em'ironm0nt. Simulationsof object manipulation demonstrates the ability to learn mUltiple objects, appropriate generalization to novel objects and the inappropriate activation of motor programs based on visual cues, followed by online correction, seen in the "size-weight illusion".

This inverse dynamics model allows the use of a faster speech mot.or control scheme, which can be applied to phoneme-tospeech synthesis via musclo-skeletal system dynamics, or to future use in speech recognition. The forward acoustic model, which is the mapping from articulator trajectories t.o the acoustic parameters, was improved by adding velocity and voicing information inputs to distinguish acollst.ic

We propose a trajectory planning and control theory for continuous movements such as connected cursive handwriting and continuous natural speech. Its hardware is based on our previously proposed forward-inverse-relaxation neural network (Wada & Kawato, 1993). Computationally, its optimization principle is the minimum torquechange criterion. Regarding the representation level, hard constraints satisfied by a trajectory are represented as a set of via-points extracted from a handwritten character. Accordingly, we propose a via-point estimation algorithm that estimates via-points by repeating the trajectory formation of a character and the via-point extraction from the character. In experiments, good quantitative agreement is found between human handwriting data and the trajectories generated by the theory. Finally, we propose a recognition schema based on the movement generation. We show a result in which the recognition schema is applied to the handwritten character recognition and can be extended to the phoneme timing estimation of natural speech. 1 INTRODUCTION In reaching movements, trajectory formation is an ill-posed problem because the hand can move along an infinite number of possible trajectories from the starting to the target point.

This inverse dynamics model allows the use of a faster speech mot.or control scheme, which can be applied to phoneme-tospeech synthesis via musclo-skeletal system dynamics, or to future use in speech recognition. The forward acoustic model, which is the mapping from articulator trajectories t.o the acoustic parameters, was improved by adding velocity and voicing information inputs to distinguish acollst.ic