Multi-aperture interferometry (MAI) and amplitude pixel offset (PO) technique are compared for the measurement of along-track surface displacements between two SAR datasets using ALOS-2 PALSAR-2 high resolution datasets. Two results have good agreement in general. The PO measurements with very high cross correlation coefficients agree well with the MAI measurements. The PO method probably performs as good as the MAI, if the cross correlation coefficients are well considered.

In this article, the effect of forest foliage in L-band SAR data is studied using images containing deciduous (beech) and evergreen (cedar) forests. Four datasets with different seasons by ALOS polarimetric PALSAR are analyzed. With respect to beech forests, more decrease of the cross-polarization backscatter power is observed after leaves fall compared to cedar forest. The effect of foliage is not large in extent but existing in L-band backscattering. From the ratios of diagonal elements in coherency matrix between August and October/December datasets show changes, which suggest changes of backscattering mechanism.

Differential SAR interferograms show displacements in only the line-of-sight direction. In order to map fully three-dimensional surface deformations, multi-interferograms from more than three directions are required. JAXA's PALSAR-2 onboard ALOS-2 has a capability to observe the Earth surface in either of right or left direction from ascending and descending tracks. In PALSAR-2 data archives, there are several interferometric pairs with different looking directions covering the suffered areas by the 2016 Kumamoto earthquakes. We attempt to depict three-dimensional surface deformations from multi-directional PALSAR-2 interferograms.

This paper proposes a power/area efficient method for realizing a class of downsampled floating tap decision feedback equalization (DFE) architectures for serial links. The architectures offer significant complexity and power reduction over a standard floating tap DFE architecture with minimal performance loss. A shift register multi-phase clock based design in 28 nm CMOS is implemented for operation at 28.05 Gb/s. At 28.05 Gb/s, the designed downsampled architecture dissipates only 67% of the power of the corresponding standard architecture operating at 17 Gb/s. At the lower 17 Gb/s speed, the downsampled architecture dissipates only 45% of the power of the standard architecture.

We intend to show the basis of a general legged locomotion controller with the ability to integrate both posture and rhythmic motion controls. We respectively used leg loading and unloading for the phase transitions from swing-to-stance and stance-to-swing, and showed the following in our previous 3D model simulation study: (a) as a result of the phase modulations based on leg loading/unloading, rhythmic motion of each leg was achieved and leg coordination (resulting in a gait) emerged, even without explicit coordination among the leg controllers, allowing to realize dynamic walking in the low- to medium-speed range (b) but an additional ascending coordination mechanism between ipsilateral leg controllers was necessary to improve the stability. In this paper, we report on experimental results using “Kotetsu” under a lateral perturbation while walking and compare them with the results of our previous simulations. Detailed robot specifications and movies of the experiments can be seen at: http://robotics.mech.kit.ac.jp/kotetsu/.

This paper proposes the use of a specific mathematical model to represent the relationship between rat behavior and sociality (degree of friendliness with the robot) in the robot-rat interaction system. To validate the model, we conducted social interaction test between a rat-like robot and real, live rats. A rat-like robot called WR-4 capable of generating friendly or neutral behaviors among rats serves as partner to the rats in the interaction. Meanwhile, we performed Principal Component Analysis to specify the mathematical model. The experimental results show that this mathematical model allows quantitative estimation of rat sociality. Likewise, the results reveal that rat sociality can be analyzed and modified in this robot-rat interaction system.

This paper proposes a class of downsampled floating tap decision feedback equalization (DFE) architectures based on downsampling of the floating tap positions. The architectures offer significant complexity and power reduction compared with a standard floating tap DFE architecture with minimal loss in performance. Simulation results with realistic channel models are used to validate the performance of these architectures.

Discrimination or separation of built-up areas is an issue in urban area analysis using POLSAR data. In this study, two polarization orientation angles from two look directions (ascending and descending orbits) are used. ALOS PALSAR data analysis shows that single look direction is insufficient and possibly leads misunderstandings. By the proposed technique, built-up areas were well discriminated, but some omission errors occur in the area where apartment houses facing to the south stand in a row. This technique can be used to prevent misleading of POLSAR decomposition and classification.

In this study, we intend to show the basis of a general legged locomotion controller with the ability to integrate both posture and rhythmic motion controls and shift continuously from one control method to the other according to the walking speed. The rhythmic motion of each leg in the sagittal plane is generated by a single leg controller which controls the swing-to-stance and stance-to-swing phase transitions using respectively leg loading and unloading information. Since rolling motion induced by inverted pendulum motion during the two-legged stance phases results in the transfer of the load between the contralateral legs, leg loading/unloading involves posture information in the frontal plane. As a result of the phase modulations based on leg loading/unloading, rhythmic motion of each leg is achieved and leg coordination (resulting in a gait) emerges, even without explicit coordination among the leg controllers, allowing to realize dynamic walking in the low- to medium-speed range. But an additional ascending coordination mechanism between ipsilateral leg controllers helps to improve the stability. In this paper, we report the result of experiments using a newly constructed quadruped robot “Kotetsu” in order to verify the results of simulations. Details of trajectory generation and movies can be seen at: http://robotics.mech.kit.ac.jp/kotetsu/.

The number of patients with mental disorders is increasing in advanced countries. Many researchers are working to develop mental disorder model animals that contribute to development of new psychotropic drugs. However, we have some doubts about conventional mental disorder models. Therefore, the purpose of this study is to develop an experimental setup to create novel mental disorder model animals. We then developed a small mobile robot and a control system for the robot. Using them, we performed an experiment to develop a mental disorder model rat. In the experiment, we succeeded in developing a new depression model rat and also high activity model rat. These disorder models must be useful in the screening of new psychotropic drugs. In addition, the methodology we developed in this research will contribute to clarifying mechanisms of mental disorders.

It is generally accepted that locomotion in animals is based on a trade-off between energy consumption and stability. However, this trade-off is the result of the interaction between complex mechanical and control systems. To gain insight into that issue, a step-by-step approach is needed. In this study, as a first step to investigate three dimensional quadrupedal walking, we aim at establishing a control system as “minimal” as possible, able to realize stable dynamic walk. Using a simple mechanical structure, we realized dynamic walk with a distributed control system, made of four independent leg controllers whose swing and stance phases durations are modulated based on leg loading information. Phase modulations contribute to stabilize the posture in the frontal plane via automatic duty ratios adjustments that tend to compensate perturbations of the body rolling motion. By applying lateral perturbations, we found that the control system withstands well perturbations increasing the rolling motion amplitude, but is sensible to perturbations that suddenly decrease it, as the foreleg on the more loaded side is prevented to swing. Hence, we implemented an ascending coordination mechanism where the transition to swing in a hind leg promotes the same event in the foreleg. The duration of the subsequent foreleg swing phase is reduced to prevent excessive increase of the rolling motion amplitude. The resultant control system, although extremely simple, was able to realize dynamic walk resistant to small disturbances (lateral perturbations and terrain irregularities).

For spaceborne polarimetric synthetic aperture radar (SAR), it is important to ensure the removal of both polarimetric system distortion and the effect of Faraday rotation. This paper proposes a new calibration method to derive the system distortion using polarization orientation (PO) induced in built-up areas and applies to Phased-Array-Type L-Band SAR (PALSAR) calibration. Faraday rotation is corrected by the circular-polarization-based method from the distortion matrix (DM)-calibrated data. The derived DMs do not coincide with those by the Japan Aerospace Exploration Agency (JAXA), but our calibration results compare well to JAXA's results in PO angles and calibrator's responses. The two results satisfy polarimetric calibration requirements, and the cross-polarized isolation improves by more than 5 dB after Faraday rotation correction following DM calibration-even in the case of small Faraday rotation (-2deg to -0.5deg). The proposed method is robust to noise and is useful when using an area of mixed polarimetric response for calibration. This method is also applicable to a large crosstalk system and the case of large Faraday rotation.

Polarization orientation angle shifts can be seen not only in rugged terrain areas but also in urban areas. The latter is explained by backscatter from a wall of a building or house, which is equivalent to a tilted ground-surface patch. From the scattering model of built-up areas, the polarization orientation angle shift in the built-up areas is given as , where is the wall or street orientation angle, and is the radar incidence angle. Japan Aerospace Exploration Agency Pi-SAR L-band polarimetric data of Gifu, Japan, show a good agreement with the theory. The phase difference between VH and HH polarizations is used to demonstrate the contribution of double-bounce scattering ground-wall and wall-ground over a wide range of wall orientation angles.

In this paper we present the system design and analysis of a quadruped robot, Rush, that we have constructed to study autonomous and efficient running on flat and rough terrain. The Rush robot is a compact, kneed, four legged machine with only one actuator per compliant leg. We have proposed a novel control strategy for the quadruped robot in consideration of several engineering limitations on sensory feedback. Several simulation studies have already been performed to confirm the validity of the control strategy in our previous reports. Here, the results obtained from experiments with Rush are found to agree with the simulation results. The work reported in this paper may help improve our understanding of energy efficient running locomotion and the simple control required to autonomously stabilize it on flat or rough terrain.

Experiments on animals has been playing very important part in psychology, medical science, genetics and brain science. Rodents are one of the most popular experimental subjects in these experiments. In psychology and brain science, behavior test and analysis on rodents, especially rats, has been performed to clarify mechanisms of mind or brain functions. "Open-field test" is one of the most popular research methods to investigate their behavior. In the "open-field test," rats are released into the "open-field," circle or square, flat arena surrounded by walls, and their behavior parameters such as position, moving distance, number of grooming and rearing are measured. With conventional experimental setups, these parameters has been measured and recorded by humans. Observing and recording are heavy task for experimenters, and also involves some human errors and subjectivity. Therefore, we developed an autonomous experimental setup which measures rats' position, number of grooming and rearing in the open-field. In addition, an active tracking camera which takes high resolution images of the rat in the open-field was developed. To prevent losing tracks of the rat, a prediction model of rat's movement is developed and integrated into the control of this camera.

For ALOS/PALSAR in orbit, removal of not only polarimetric system distortion but also the effect of Faraday rotation is important. The author investigated a new calibration method using polarization orientation induced in build-up areas. Its fundamental concept is simple and is that two polarization orientation angles from two polarization combinations should be identical after calibration. This method is applied to simulated data that is distorted from JAXA L-band Pi-SAR data. An advantage of this method is that strong backscatters from buildup areas can be used to decrease an influence of noise.

The learning from observation (LFO) paradigm has been widely applied in various types of robot systems. It helps reduce the work of the programmer. However, the applications of available systems are limited to manipulation of rigid objects. Manipulation of deformable objects is rarely considered, because it is difficult to design a method for representing states of deformable objects and operations against them. Furthermore, too many operations are possible on them. In this paper, we choose knot tying as a case study for manipulating deformable objects, because the knot theory is available and the types of operations possible in knot tying are limited. We propose a knot planning from observation (KPO) paradigm, a KPO theory, and a KPO system.

We report on the design and stability analysis of a simple quadruped running controller that can autonomously generate steady running with good energy efficiency and suppress such disturbances as irregularities of terrain. The self-stabilization property of the mechanical system is the inspirational source for our idea. We propose an original Delayed Feedback Control (DFC) approach based on measurements of the stance phase period obtained from contact sensors. Here, the DFC approach will not only stabilize running locomotion around the fixed point, but also result in transition from the stand state to the steady bounding running. Finally, we show several simulation results on different terrain (e.g., flat and step) to characterize the performance of the proposed controller. MPEG footage of simulations can be seen at: http://www.kimura.is.uec.ac.jp/running

Faraday rotation will be an issue of L-band spaceborne polarimetic SAR. Two approaches to calibrate data affected by Faraday rotation are described. In both approaches the cross-talk of radar system is involved. The first one is based on the Quegan method, and the second one is to use backscattering property of build-up areas, whose polarization orientation angle shift is related to the rotation angle of the wall of a building or a house.

In this paper, we employed a conservative spring-loaded two-segment legged model to test the effect of passive dynamics on running stability. By numerical return map studies, we investigated system stability and discovered that passive generation of a large variety of cyclic jumping motion on the legged model is possible. The results of this study indicated that the dynamics of suitable mechanical system could significantly alone improve the stability of legged running and suggest that swing phase dynamics may play an important role in adjustment of forward velocity and apex height. Moreover, we used the spring-loaded two segment legged model as a template to construct a simplified model of our Tekken I quadruped robot on numerical simulation. Using the constructed simulator to investigate the quadruped robot system stability, we proposed a simple control method for quadrupedal bounding and verified its effectiveness. In addition, our studies relating to passive dynamics of quadruped robot might help to understand the reliable stability and remarkable maneuverability on our experimental robot.

From a series of three JERS-1 SAR interferograms over the Yamato mountains area in the Antarctica, ice surface movement is detected. In spite of less accurate orbit information, baselines are estimated using 1 km global DEM and referring phases over bare rock areas. The Detected movement fields from three interferograms resemble, but different subtly.

The polarimetric cross-talk calibration algorithm proposed by Quegan, which is more general than the one proposed by van Zyl, is modified to improve polarimetric SAR calibration. L-band polarimetric SAR data acquired by the JAXA/NICT airborne Pi-SAR, including a variety of natural and artificial targets, are calibrated introducing noise imbalance of cross-polarized channels into the Quegan algorithm and deriving the cross-talk parameters from areas with low correlation between like and cross-polarized observed echoes. The achieved enhancement in stability of cross-talk parameter estimation and removal over is demonstrated in terms of CR response analysis and polarimetric classification result

We have been trying to induce a quadruped robot to walk with medium walking speed on irregular terrain based on biological concepts. We propose the essential conditions for stable dynamic walking on irregular terrain in general, and we design the mechanical system and the neural system by comparing biological concepts with those essential conditions described in physical terms. PD-controller at joints constructs the virtual spring-damper system as the visco-elasticity model of a muscle. The neural system model consists of a CPG (central pattern generator), reflexes and responses. A CPG generates rhythmic motion for walking. We define a "reflex" as joint torque generation based on sensor information and a "response" as CPG phase modulation through sensory feedback to a CPG. The state of the virtual spring-damper system is switched based on the phase signal of the CPG. CPGs, the motion of the virtual spring-damper system of each leg and the rolling motion of the body are mutually entrained through the rolling motion feedback to CPGs, and can generate adaptive walking. We report our experimental results of dynamic walking on irregular terrain in outdoor environment using a self-contained quadruped robot in order to verify the effectiveness of the designed neuro-mechanical system.

In this paper, we propose a non-invasive and nonrestrictive human vital-signs sensory system using an electrically-conductive fiber. To maintain health, it is important to monitor our physical condition daily. Even during sleep, a person's condition is not constant and sometimes it can change suddenly. Therefore, we are developing a human vital-signs sensory system for monitoring purposes. Our proposed system consists of a sensing part, data collection part and signal processing part. The sensing part is composed of electrically-conductive fibers incorporated in a conventional bed sheet. Since the sensors are arranged in a grid structure, we can detect the pressure distribution on the sheet. We utilize this sheet to acquire human vital signs such as respiration and heart rate continuously. With a preliminary prototype, we show the basic properties of the proposed sensory system.

We have induced a quadruped robot to walk with medium walking speed on irregular terrain based on biological concepts. The PD-controller at joints constructs the virtual spring-damper system as the visco-elasticity model of a muscle. The neural system model consists of a CPG (central pattern generator) and reflexes. A CPG receives sensory input and changes the period of its own active phase. The desired angle and P-gain of each joint in the virtual spring-damper system is switched based on the phase signal of the CPG. CPGs, the motion of the virtual spring-damper system of each leg and the rolling motion of the body are mutually entrained through the rolling motion feedback to CPGs, and can generate adaptive walking. We report our experimental results of dynamic walking on terrains of medium degrees of irregularity in order to verify the effectiveness of the designed neuro-mechanical system.

Learning from Observation (LFO) has been widely applied in various types of robot system. It helps reduce the work of the programmer. But the available systems have application limited to rigid objects. Deformable objects are not considered because: 1) it is difficult to describe their state, and 2) too many operations are possible on them. In this paper, we choose the knot tying as case study for operating on nonrigid bodies, because a "knot theory" is available and the type of operations is limited. We describe the Knot Planning from Observation (KPO) paradigm, KPO theory and KPO system.

To learn a new everyday task under the "Learning from Observation" framework, the system needs to detect which parts of the demonstration are essential to complete the task without task-dependent knowledge. In the previous research, we proposed a technique to estimate essential interactions in a task by integrating multiple demonstrations which represent virtually the same task. Although, the technique could automatically segment the essential interactions and determine the number of the interactions, the segmentation algorithm depends on some heuristics and only stationary interactions could be obtained. In this paper, a novel technique is proposed, which overcomes this limitation and can estimate almost any types of interactions. In this approach, a demonstrator needs to give a explicit signal once during each essential interaction as a hint on the occurrence of the essential interaction. From visual information and these signals, the system automatically analyzes the essential parts of the task and their periods, and also detects which environmental objects are interacted with the manipulated object. These information is hard to be obtained from a single demonstration, because of the ambiguity in interpreting the interaction especially in cluttered environment. The proposed method is evaluated in a simulation and also in a real world by using a humanoid robot.

Various methods to recognize assembly tasks using possible local displacement of objects have been proposed. To calculate this displacement, the screw theory is employed. It is equivalent to the first order Taylor expansion of the displacement. However, such methods can treat polyhedral objects only. Because the screw theory cannot treat curvature information of objects. In this paper, we propose a method to calculate possible local displacement of curve objects using improved screw theory, which is equivalent to the second order Taylor expansion of the displacement, and verify the validity of the proposed method.

This paper presents a new neural system and mechanical system for quadruped robots with compliant legs, a spring mechanism located at the ankle joint. PD-controller at hip and knee joints constructs the virtual spring-damper system as the visco-elasticity model of a muscle. The neural system model consists of a CPG (Central Pattern Generator) and reflexes. A CPG changes the own active phase based on sensory information. The desired angle and P-gain of each joint in the virtual spring-damper system is switched based on the phase signal of the CPG. The pitching motion of the body is entrained into the CPG as a sensory feedback. Therefore, adaptive running can be generated. Some experimental results of running on terrain of low degrees of irregularity are used to verify the effectiveness of the designed neuro-mechanical system.

We attempt to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model consisting of a central pattern generator (CPG) and reflexes. In this paper, we report on a newly developed quadruped robot, which contains a mechanism designed to make adaptive 3D space walking (pitch, roll and yaw planes) on irregular terrain be performed more simply. In 3D walking, a rolling motion is naturally generated in most gaits. By having the rolling motion be the standard oscillation and making the CPGs of the legs be entrained with a rolling motion, we realized the stabilization of the gait at a constant walking speed, in spite of an unbalanced gravity load between the fore and hind legs. We also realized an autonomous gait transition in changing walking speeds, by utilizing such rolling motion feedback to CPGs, in order to reduce energy consumption. Rolling motion feedback to CPGs contributes to gait stabilization in walking on an irregular terrain as a tonic labyrinthine reflex for rolling. At present stage, the robot succeeded in walking over several 2D irregular terrains by using CPGs, a flexor reflex and tonic labyrinthine reflexes for pitching and rolling.

This paper describes a new approach on how to teach a robot everyday manipulation tasks under the "learning from observation" framework. Most of the approaches so far assume that a demonstration can be well understood from a single demonstration. However, a single demonstration contains ambiguity, in that interactions which are essential to complete a task cannot be discerned without prior task dependent knowledge, which should be obtained from observation. To address these issues, we propose a technique to integrate multiple observations of demonstrations. The demonstrations differ, but are virtually the same task. The shared interactions among all the demonstrations are considered to be essential and we form a task model from their symbolic representations. Then the relative trajectories corresponding to each essential interaction are generalized by calculating their mean and variance and are also stored in the task model, which is used to reproduce a skilled behavior. We examine this approach by using a human-form robot, which successfully imitates human demonstrations of everyday tasks.

Calibration and application of polarimetric synthetic aperture radar (SAR) onboard the Advanced Land Observing Satellite (ALOS) is discussed using airborne L-band SAR data. We find that even a simple calibration is useful for polarimetric classification, if the system has a small cross-talk and good channel valance.

Tracking a target robustly by utilizing vision is very difficult for a mobile robot running over the irregular terrain of a natural environment, due to the image deformation caused by the rolling and pitching of the camera, as well as due to the relative movement between the target and the camera. One approach for coping with such problems is matching the target image with many affine-transformed candidate images while tracking. However, when the number of candidate images increases significantly, such an approach is not practical to real-time tasks due to the high computational cost involved. In the present paper, we propose a new system called robustness analysis for tracking (RAT) that improves tracking ability. RAT is an analysis based on the features of the object image, in which three parameters - detectability, robustness for depth and robustness for rotation - are defined. Many more robust templates can be found by analyzing the object image using RAT before the tracking task is performed. The experimental results are shown to verify the effectiveness of this method.

This paper describes a new approach on how to teach everyday manipulation tasks to a robot under the "Learning from Observation" framework. In our previous work, to acquire low-level action primitives of a task automatically, we proposed a technique to estimate essential interactions to complete a task by integrating multiple observations of similar demonstrations. But after many demonstrations are performed, there may be interactions which are the same in nature. These identical interactions should be grouped so that each action primitive becomes unique. For this purpose, a Hidden Markov Model based clustering algorithm is presented which automatically determines the number of independent interactions. We also show that the obtained interactions can be used as discriminators of human behavior. Finally, simulation and experimental results in which a real humanoid robot learns and recognizes essential actions by observing demonstrations are presented.

In order to transmit, share and store human knowledge, it is important for a robot to be able to acquire task models and skills by observing human actions. Since vision plays an important role for observation, we propose a technique for measuring the position and posture of objects and hands in 3-dimensional space at high speed and with high precision by vision. Next, we show a framework for the analysis and description of a task by using an object functions as elements.

The learning-from-demonstration method is considered for a novel robot-programming style. It consists of two parts: 1) to recognize human performance from observation as sequential motion primitives; and 2) to execute the same performance. We (2000) have proposed a method to recognize assembly tasks. However, the execution requires the ability to convert motion primitives to collision free paths. In this paper, we describe a method to calculate collision free paths. Many researchers have proposed to calculate collision free paths using analytical methods, potential fields or probabilistic methods. Potential and probabilistic methods are very powerful tools on a computer, but their solutions are not optimal. We propose a method to calculate optimal collision free paths analytically.

The local displacement of an object is very useful for deciding grasp stability, generating trajectories, recognizing assembly tasks, and so on. To calculate this displacement, the screw theory is employed. It is equivalent to the first order Taylor expansion of the displacement. The screw theory is very convenient, because the displacement is formulated as simultaneous linear inequalities, and a powerful tool to calculate such inequalities, the theory of the polyhedral convex cones, has already been established. However, truncation errors introduced by first order approximations sometimes cause mistaken results. In this paper, we improve the screw theory by using 2nd order terms and verify the validity of the result.

The completion of robot programs requires long development time and much effort. To shorten the programming time and to minimize the effort, we have been developing a system which we refer to as the "assembly-plan-from-observation (APO) system." This system requires assembly task recognition from observing human performance. Observation data by a robot's vision system is usually error contaminated, and, thus, we cannot use those data directly. This paper proposes two methods to clean up those errors by using contact relations and their transitions. The first one corrects the observed configuration from contact relations observed. The second one identifies wrongly determined contact relations from an analysis of configuration space (C-space). We have implemented both methods on our test bed and have verified their effectiveness.

This paper describes our current research on learning task level representations by a robot through observation of human demonstrations. We focus on human hand actions and represent such hand actions in symbolic task models. We propose a framework of such models by efficiently integrating multiple observations based on attention points; we then evaluate the model by using a human-form robot. We propose a two-step observation mechanism. At the first step, the system roughly observes the entire sequence of the human demonstration, builds a rough task model and extracts attention points (APs). The attention points indicate the time and position in the observation sequence that requires further detailed analysis. At the second step, the system closely examines the sequence around the APs and the obtained attribute values for the task model, such as what to grasp, which hand to be used, or what is the precise trajectory of the manipulated object. We implemented this system on a human form robot and demonstrated its effectiveness.

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. In our previous study, we integrated several reflexes into a CPG (central pattern generator) and realized adaptive 2D walking on terrain of medium degree of irregularity. In this paper, in order to make the role of a CPG be clear, we investigate the relation between parameters of a CPG and the mechanical system by simulations and experiments. In addition, we show a newly developed quadruped robot, of which mechanism is designed to make adaptive 3D walking on irregular terrain be realized more simply. At this moment, 3D walking on flat terrain is realized by using a neural system model consisting of CPG and reflexes.

Describes research on learning task level representations by a robot through observation of human demonstrations. We focus on human hand actions and develop a construction method of a human task model which integrates multiple observations to solve ambiguity based on attention points (AP). So far, this analysis constructs a symbolic task model efficiently in a coarse-to-fine way through two steps. However, to represent delicate motion appearing in a task, the system must incorporate the information about precise motion of the manipulated objects into an abstract task model. We propose a method to identify the manipulated object through repeated observations of both human and robot behavior. To this end, we present a method which combines 2D and 3D template matching techniques to localize an object in 3D space generated from a depth and an intensity image. We apply this technique to recognition of human and robot behavior by obtaining the precise trajectory of the manipulated objects. We also present the experimental results achieved through the use of a human-form robot equipped with a 9-eye stereo vision system.

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. In this paper, we integrate several reflexes such as stretch reflex, vestibulospinal reflex, and extensor and flexor reflex into CPG (central pattern generator). The success in walking on terrain of medium degree of irregularity with fixed parameters of CPG and reflexes shows that the biologically inspired control proposed in this study has an ability for autonomous adaptation to unknown irregular terrain.

The completion of robot programs requires long development time and much effort. To shorten this programming time and minimize the effort, we have been developing a system which we refer to as "assembly-plan-from-observation (APO) system;" this system provides the ability for a robot to observe a human performing an assembly tasks, understand the tasks, and subsequently generate a program to perform that same task. One of the necessary tasks in APO is to create a trajectory of robot hand movement from observing human performance. The previous system developed a direct observation method based on the trajectory of a human movement. Though simple and handy, the system was susceptible to noise. This paper proposes a method to make the observation robust against noise by using symbolic representations of a trajectory based on contact analysis. The system divides the trajectory into small segments based on the contact analysis, then allocates an operation element referred to as a sub-skill to those segments; the result is a robust trajectory-based APO system.

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. We integrate several reflexes, such as a stretch reflex, a vestibulospinal reflex, and extensor/flexor reflexes, into a CPG (central pattern generator). The success in walking on terrain of medium degree of irregularity with fixed parameters of CPGs and reflexes shows that the biologically inspired control proposed in this study has an ability for autonomous adaptation to unknown irregular terrain.

This paper presents a novel method of constructing a human task model by attention point (AP) analysis. The AP analysis consists of two steps: at the first step, it broadly observes human task, constructs rough human task model and finds APs which require detailed analysis; and at the second step, by applying time-consuming analysis on APs in the same human task, it can enhance the human task model. This human task model is highly abstracted and is able to change the degree of abstraction adapting to the environment so as to be applicable in a different environment. We describe this method and its implementation using data gloves and a stereo vision system. We also show an experimental result in which a real robot observed a human task and performed the same human task successfully in a different environment using this model.

The completion of robot programs requires a long development time and much effort. To shorten this programming time and to minimize the effort, we have been developing a system which we refer to as the "Assembly Plan from Observation (APO) system". This system provides the ability for a robot to observe a human performing an assembly task, to recognize the task and to then generate a program to perform that same task. One of the necessary tasks in APO is to create the trajectory of a robot hand movement after having observed a human's performance. The previous system developed a direct observation method based on the trajectory of a human's movement. Although it was simple and handy, the system was susceptible to noise. This paper proposes a method to make the observation robust against noise by analyzing topological contact relations. The system divides the trajectory into small segments based on this contact analysis, and then allocates an operation element, referred to as a "sub-skill", to those segments. The result is a robust, trajectory-based APO system.

We attempt to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. In the paper, in order to realize walking on irregular terrain, we propose the biologically-inspired control method consisting of four levels, these are: "adaptive control using a muscle stiffness model", "adaptive control based on vestibular sensation", "parameters adjustment based on somatic sensation and reflexes coordination based on vestibular sensation", and "motion switching based on visual information". As results of basic experiments of each level, we show that a robot can walk on a single bump, and walk up and dozen a slope by using such adaptive control method. We also show that the functions as a lower controller in the Drew's physiological model for leg control mechanism based on visual information are satisfied by a CPG characterized by mutual entrainment with a musculo-skeletal system, automatic interpolation, and self stabilization. These findings suggest a simple method for producing autonomous adaptive dynamic walking on terrain of high degree irregularity.

In order to assist a human, the robot must recognize human motion in real time by vision, and must plan and execute the needed assistance motion based on the task purpose and the context. In this research, we tried to solve such problems. We defined the abstract task model, analyzed the human demonstration by using events and an event stack, and automatically generated the task models needed in the assistance by the robot. The robot planned and executed the appropriate assistance motions based on the task: models according to the human motions in the cooperation with the human. We implemented a 3D object recognition system and a human grasp recognition system by using trinocular stereo color cameras and a real time range finder. The effectiveness of these methods was tested through an experiment in which the human and the robotic hand assembled toy parts in cooperation.

The purpose of this study is to realize a multiple robot system which can be easily developed and managed. In order to perform robust cooperative work, it is necessary that multiple robots can achieve not only a high level of cooperation without communication but also simple cooperation with communication, and then switch between modes according to the situation. We call such cooperative work "tight cooperation". To achieve such cooperation, there must be an integrated concept in the system about structure in a robot and whole system. We propose a method for the construction of such a multiple robot system based on this concept. We constructed a cooperative working system called ICRoS and implemented it on two hexapod robots with parallel processors. We verified the effectiveness of this multiple robot system with four different experiments involving the cooperative lifting of a box.

In a cooperation style in which a human operates one robot and the other autonomous robots assist that robot, the autonomous robots must be able to recognize the motion of the human operated robot in real time. Vision is the most useful sensor for this purpose. By using vision, the assistant robot recognizes the current target object and actions of the human operated robot to the target. For the working robots, we use two hexapod robots which are able to utilize two forelegs as arms in order to manipulate objects. Joint motion of each leg and occlusion by another leg makes it difficult to recognize and track the motion of the hexapod robot. We propose a method for the recognition and tracking of the 3D position and posture of the hexapod. Through the use of 2D image matching, the body of the hexapod, several legs, and toes of the legs can be recognized in turn. The positions of the body and toes are measured by binocular-stereo, and the angles of the legs can then be obtained, which are used for motion recognition. We propose a motion recognition mechanism using an eigenspace method which can reduce the dimensions necessary for matching. The effectiveness of this method is tested through an experiment using two hexapod robots.

We attempt to induce a quadruped robot to walk dynamically on irregular terrain and run on flat terrain by using a nervous system model. For dynamic walking on irregular terrain, we employ a control system involving a neural oscillator network, a stretch reflex and a flexor reflex. Stable dynamic walking when obstructions to swinging legs are present is made possible by the flexor reflex and the crossed extension reflex. A modification of the single driving input to the neural oscillator network makes it possible for the robot to walk up a step. For running on flat terrain, we combine a spring mechanism and the neural oscillator network. It became clear in this study that the matching of two oscillations by the spring-mass system and the neural oscillator network is important in enabling the robot to hop. The present study also shows that entrainment between neural oscillators causes the running gait to change from hopping to bouncing. This finding renders running fairly easy to attain in a bounce gait. It must be noticed that the flexible and robust dynamic walking on irregular terrain and the transition of the running gait are realized by the modification of a few parameters in the neural oscillator network.

The purpose of this research is to make multiple robots perform a designated task according to the intentions of a human operator. Since operating multiple robots simultaneously is difficult for a human operator, we propose a cooperation style in which the human operates one robot and the other autonomous robots assist it. In order to assist the human operating robot, the autonomous robots must recognize its motions in real time. In this paper, we propose the same motion recognition mechanism that is used in the human cognition model. We implement the motion symbolization method and the motion recognition method using the fuzzy filter and the template matching with predefined patterns. We implement the "task-operation model" to describe the motion of the autonomous robot's assistance mechanism, and the "event driven method" to manage the execution of this motion. The effectiveness of these methods is tested through an experiment in which two hexapod robots lift a box in cooperation.

The authors present the design of an advanced software development system for dozens of robots with tight cooperation among robots. The system is based on Smalltalk-80 and extended for the purpose of the multiple robots control. The principle for the extension are very simple, making it easy to implement virtual machines for the multiple processors and the multiple robots.

The development of an intelligent fixture for automatic assembly by the robots that can manipulate and fix a heavy object in three dimensions and make the relocation of the jigs unnecessary is addressed. A new mechanism, which is constructed by the rotational base and the one-DOF fingers, is proposed. It makes the manipulation (mainly rotation) of the heavy target object possible because of its simplicity, but the simplicity causes some problems. The kinematics and the dynamics of manipulation by this mechanism is derived, and it is shown how to plan the manipulation. Simulation and experimental results are reported.

A convenient method for measuring synthetic-aperture radar (SAR) azimuth antenna patterns and microwave penetration of soil or trees using spectrum analyzers and amateur-radio antennas is described. The received power of a ground receiver is estimated for several SARs. This estimation suggests that spectrum analyzers can be used for ground measurement during SAR observation. Results of in situ experimental measurements which were made during the NASA/JPL DC-8 SAR campaign in summer 1990 in the JAROS/NASA JERS-1 simulation project are presented. A discussion and perspectives on applications for JERS-1 SAR are included.>

The authors propose a type of jig which can automatically manipulate an object into a desired position and orientation. The jig consists of multifingered hands and jig base. The characteristics of the jig are that multifingered hands with one degree of freedom are arranged. Its advantages are that the mechanism is simple and that heavy objects can be manipulated. The disadvantage is that sliding occurs between the object and fingertip. The manipulations considered are in large motions of an object with sliding contact by simple mechanism multifingered hands. The kinematics and dynamics of manipulation involving sliding in the plane are discussed. The manipulation planning concerning kinematics and dynamics is presented. The results of the simulation are illustrated.>