Koichi Ogawara

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The Programming by Demonstration (PbD) technique aims at teaching a robot to accomplish a task by learning from a human demonstration. In a manipulation context, recognizing the demonstrator's hand gestures, specifically when and how objects are grasped, plays a significant role. Here, a system is presented that uses both hand shape and contact-point(More)
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(More)
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(More)
Learning from Obsemation (LFO) has been widely applied in varions 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: l ) it is dificult to describe their state and 2) too many operations are possible on them. In this paper, we(More)
This paper presents a novel whole body motion estimation method by fitting a deformable articulated model of the human body into the 3D reconstructed volume obtained from multiple video streams. The advantage of the proposed method is two fold: (1) combination of a robust estimator and ICP algorithm with Kd-tree search in pose and normal space make it(More)
A technique to recognize the shape of a grasping hand during manipulation tasks is proposed; which utilizes a 3D articulated hand model and a reconstructed 3D volume from infrared cameras. Vision-based recognition of a grasping hand is a tough problem, because a hand may be partially occluded by a grasped object and the ratio of occlusion changes along the(More)
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. But a single demonstration contains ambiguity, in that interactions which are essential to complete a task can’t(More)
As one of the methods for reducing the work of programming, the Learning-from-Observation (LFO) paradigm has been heavily promoted. This paradigm requires the programmer only to perform a task in front of a robot and does not require expertise. In this paper, the LFO paradigm is applied to assembly tasks by two rigid polyhedral objects. A method is proposed(More)