Faculty Profiles - OGAWARA Koichi

Published Papers - OGAWARA Koichi

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A photo-realistic driving simulation system for mixed-reality traffic experiment space

Shintaro Ono, Koichi Ogawara, Masataka Kagesawa, Hiroshi Kawasaki, Masaaki Onuki, Junichi Abeki, Tori Yano, Masami Nerio, Ken Honda, Katsushi Ikeuchi

IEEE Intelligent Vehicles Symposium, Proceedings 2005 747 - 752 2005 [Refereed]

In this paper, we propose an efficient and effective image generation system for "Mixed Reality Traffic Experiment Space", an enhanced driving/traffic simulation system which we have been developing for Sustainable ITS project at the University of Tokyo. Conventional driving simulators represent ther view by a set of polygon-based objects, which leads to less photo-reality and huge human costs for dataset construction. We introduce our image/geometry-based hybrid method to realize more photo-realistic view with less human cost at the same time. Images for datesets are captured from real world by multiple video cameras mounted on a data acquisition vehicle. And the view for the system is created by synthesizing the image dataset. Following contents mainly describe details on data acquisition and view rendering. © 2005 IEEE.

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Driving view simulation synthesizing virtual geometry and real images in an experimental mixed-reality traffic space

Shintaro Ono, Koichi Ogawara, Masataka Kagesawa, Hiroshi Kawasaki, Masaaki Onuki, Ken Honda, Katsushi Ikeuchi

Proceedings - Fourth IEEE and ACM International Symposium on Symposium on Mixed and Augmented Reality, ISMAR 2005 2005 214 - 215 2005 [Refereed]

We propose an efficient and effective image generation system for an experimental mixed-reality traffice space. Our enhanced traffic/driving simulation system represents the view through a hybrid that combines virtual geometry with real images to realize high photo-reality with little human cost. Images for datasets are captured from the real world, and the view for the simulation system is created by synthesizing image datasets with a conventional driving simulator. © 2005 IEEE.

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Understanding of Human Assembly Tasks for Robot Execution Generation of Optimal Trajectories Based on Transitions of Contact Relations

TAKAMATSU Jun, OGAWARA Koichi, KIMURA Hiroshi, IKEUCHI Katsushi

JRSJ ( The Robotics Society of Japan ) 22 ( 6 ) 752 - 763 2004.09 [Refereed]

The <I>planning-from-observation</I> paradigm is widely noticed as a novel robot-programming technique. It consists of two parts: (1) recognition of a human demonstration from observation as symbolic representation, i.e., a sequence of movement primitives and (2) execution of the same task. Symbolic representation enables a robot to achieve the same task even in a different environment. We already proposed a method to build a symbolic representation for an assembly task. However, for a robot to execute the task, it is necessary to adjust parameters of each movement primitive to generate appropriate trajectories of objects, especially a path which maintains one of any contact states. Many researchers have proposed methods to calculate such a path using a non-linear optimization method, for example, the potential field method, the probabilistic method, and so on. Although these methods are very powerful as calculation tools on a computer, their solutions are not optimal. We propose a novel method to calculate such an optimal path using a linear solution.

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民俗芸能のデジタルアーカイブとロボットによる動作提示

池内克史, 中澤篤志, 小川原光一, 高松淳, 工藤俊亮, 中岡慎一郎, 白鳥貴亮

日本バーチャルリアリティ学会誌 9 ( 2 ) 14 - 20 2004.06 [Refereed]

Extraction of Essential Interactions Through Multiple Observations of Human Demonstrations

Koichi Ogawara, Jun Takamatsu, Hiroshi Kimura, Katsushi Ikeuchi （Part： Lead author )

IEEE Transactions on Industrial Electronics 50 ( 4 ) 667 - 675 2003.12 [Refereed]

観察に基づく手作業の獲得における視覚の利用

小川原光一, 高松淳, 木村浩, 池内克史（Part： Lead author )

情報処理学会論文誌コンピュータビジョンとイメージメディア 44 ( SIG 17 ) 13 - 23 2003.12 [Refereed]

Knot planning from observation

Takuma Morita, Jun Takamatsu, Koichi Ogawara, Hiroshi Kimura, Katsushi Ikeuchi

Proceedings - IEEE International Conference on Robotics and Automation 3 3887 - 3892 2003

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, a KPO theory and a KPO system.

Acquisition of a symbolic manipulation task model by attention point analysis

Koichi Ogawara, Jun Takamatsu, Hiroshi Kimura, Katsushi Ikeuchi （Part： Lead author )

Advanced Robotics 17 ( 10 ) 1073 - 1091 2003 [Refereed]

As a way of automatic programming of robot behavior, a method for building a symbolic manipulation task model from a demonstration is proposed. The feature of this model is that it explicitly stores the information about the essential parts of a task, i.e. interaction between a hand and an environmental object, or interaction between a grasped object and a target object. Thus, even in different environments, this method reproduces robot motion as similar as possible to that of humans to complete the task while changing the motion during non-essential parts to adapt to the current environment. To automatically determine the essential parts, a method called attention point analysis is proposed
this method searches for the nature of a task using multiple sensors and estimates the parameters to represent the task. A humanoid robot is used to verify the reproduced robot motion based on the generated task model.

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複数教示動作の時系列上での統合に基づく人間作業のモデル化手法

小川原光一, 高松淳, 木村浩, 池内克史（Part： Lead author )

情報処理学会論文誌コンピュータビジョンとイメージメディア 43 ( SIG 4 ) 117 - 126 2002.06 [Refereed]

Modeling manipulation interactions by hidden Markov models

Koichi Ogawara, Jun Takamatsu, Hiroshi Kimura, Katsushi Ikeuchi

IEEE International Conference on Intelligent Robots and Systems 2 1096 - 1101 2002

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 are possibly 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 the independent interactions, We also show that obtained interactions can be used as discriminations of human behavior. Finally, a simulation result and an experimental result in which a real humanoid robot learns and recognizes essential actions by observing demonstrations are presented.

Correcting observation errors for assembly task recognition

Jun Takamatsu, Koichi Ogawara, Hiroshi Kimura, Katsushi Ikeuchi

IEEE International Conference on Intelligent Robots and Systems 1 232 - 237 2002

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.

Refining hand-action models through repeated observations of human and robot behavior by combined template matching

Koichi Ogawara, Hiroshi Kimura, Katsushi Ikeuchi

IEEE International Conference on Intelligent Robots and Systems 1 545 - 550 2001

This paper describes our current research on learning task level representations by a robot through observation of human demonstrations. Representations proposed so far typically utilized one or a few sensors and were constructed through one time observation, therefore they could solve ambiguity in the data only under strong assumptions. We focus on human hand actions and have been developing a novel construction method of a human task model which integrates multiple observations to solve ambiguity based on Attention Points (APs). So far, this analysis constructs a symbolic task model efficiently in a coarse-to-fine way through two steps. However, to represent delicate motion appeared in a task, the system must incorporate the information about precise motion of the manipulated objects into an abstract task model. In this paper, 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.

Acquiring hand-action models by attention point analysis

Koichi Ogawara, Soshi Iba, Tomikazu Tanuki, Hiroshi Kimura, Katsushi Ikeuchi

Proceedings - IEEE International Conference on Robotics and Automation 1 465 - 470 2001 [Refereed]

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 produced 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 also extracts attention points (APs). The attention points indicate the time and the position in the observation sequence that requires further detailed analysis. At the second step, the system closely examines the sequence around the APs, and obtains 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 have implemented this system on a human form r obot and demonstrated its effectiveness.

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Extraction of fine motion through multiple observations of human demonstration by DP matching and combined template matching

Koichi Ogawara, Jun Takamatsu, Hiroshi Kimura, Katsushi Ikeuchi

Proceedings - IEEE International Workshop on Robot and Human Interactive Communication 8 - 13 2001 [Refereed]

This paper describes our current research on how a robot, through observation of human demonstrations, can learn task level representations of human hand-work tasks. Representations proposed so far typically handle a human hand trajectory as it is or segment the entire task into a discrete pre-determined symbol sequence
but to make a generalized model of human hand-work tasks, both types of information must be incorporated into the model appropriately. We propose a technique for segmenting an observed hand-work task into pieces which are composed of fine motion or coarse motion. Fine motion means delicate manipulation and holds the relative trajectory between the grasped object and the target object, while coarse motion is a symbol which connects each fine motion. During coarse motion, a trajectory can be adjusted according to the environment or the structure of a robot when the robot performs the same task performed by a human. To extract essential fine motion automatically, we propose a technique for integrating and aligning multiple observations of different demonstrations, which are virtually the same task, by using data gloves and multi-dimensional Dynamic Programming (DP) matching. Along each fine motion, the relative trajectory (position and orientation) is calculated by tracking the manipulated object using a stereo vision. To localize and track the manipulated object efficiently, we propose a model-based localization technique, which combines 2D and 3D template matching. We have implemented those techniques on our human-form robot and present an experimental result which analyzed and performed a non-contact hand-work task. © 2001 IEEE.

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ネットワーク環境におけるモバイルエージェント機構に基づいて個人の継続的な支援を目指した分散ロボット統合法

小川原光一, 金広文男, 稲葉雅幸, 井上博允（Part： Lead author )

日本ロボット学会誌 18 ( 7 ) 1034 - 1039 2000.07 [Refereed]

Recognition of Human Behaviour using Stereo Vision and Data Gloves

Ogawara Koichi, Iba Soshi, Tanuki Tomikazu, Sato Yoshihiro, Saegusa Akira, Kimura Hiroshi, Ikeuchi Katsushi

Monthly journal of the Institute of Industrial Science, University of Tokyo ( The University of Tokyo ) 52 ( 5 ) 225 - 230 2000.05

This paper presents a novel method of constructing a human behaviour model by attention point (AP) analysis. The AP analysis consists of two steps. At the first step, it broadly observes human behaviour, constructs rough human behaviour model and finds APs which require detailed analysis. Then at the second step, by applying time-consuming analysis on APs in the same human behaviour, it can enhance the human behaviour model. This human behaviour 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 and performed the same human behaviour successfully in a different environment using this model.

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Recognition of human task by attention point analysis

Koichi Ogawara, Soshi Iba, Tomikazu Tanuki, Hiroshi Kimura, Katsushi Ikeuchi

IEEE International Conference on Intelligent Robots and Systems 3 2121 - 2126 2000 [Refereed]

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. Then 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.

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Symbolic Representation of Trajectories for Skill Generation.

Hirohisa Tominaga, Jun Takamatsu, Koichi Ogawara, Hiroshi Kimura, Katsushi Ikeuchi

Proceedings of the 2000 IEEE International Conference on Robotics and Automation, ICRA 2000, April 24-28, 2000, San Francisco, CA, USA ( IEEE ) 4076 - 4081 2000 [Refereed]

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Published Papers - OGAWARA Koichi