A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models

Bernardin, Keni; Ogawara, Koichi; Ikeuchi, Katsushi; Dillmann, Ruediger

doi:10.1109/tro.2004.833816

Cited by 113 publications

(47 citation statements)

References 14 publications

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“…In robot LbD, a common approach to segmenting actions is based on hidden markov models (HMM). For example, [5] discriminated hand-grasping gestures from continuous actions. Using HMMs, [7] and [2] also identified boundaries in action sequences.…”

Section: Related Workmentioning

confidence: 99%

Human-like action segmentation for option learning

Shim

Thomaz

2011

2011 Ro-Man

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Abstract-Robots learning interactively with a human partner has several open questions, one of which is increasing the efficiency of learning. One approach to this problem in the Reinforcement Learning domain is to use options, temporally extended actions, instead of primitive actions. In this paper, we aim to develop a robot system that can discriminate meaningful options from observations of human use of low-level primitive actions. Our approach is inspired by psychological findings about human action parsing, which posits that we attend to low-level statistical regularities to determine action boundary choices. We implement a human-like action segmentation system for automatic option discovery and evaluate our approach and show that option-based learning converges to the optimal solutions faster compared with primitive-action-based learning.

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Section: Related Workmentioning

confidence: 99%

Human-like action segmentation for option learning

Shim

Thomaz

2011

2011 Ro-Man

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“…Researchers used HMM for situational awareness for traffic monitoring (Bruckner et al, 2007) and learning hand grasping movements for robots (Bernardin et al, 2003).…”

Section: Hmm Framework For Situational Awarenessmentioning

confidence: 99%

Hidden Markov Model as a Framework for Situational Awareness

Damarla¹

2010

Sensor Fusion and Its Applications

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In this chapter we present a hidden Markov model (HMM) based framework for situational awareness that utilizes multi-sensor multiple modality data. Situational awareness is a process that comes to a conclusion based on the events that take place over a period of time across a wide area. We show that each state in the HMM is an event that leads to a situation and the transition from one state to another is determined based on the probability of detection of certain events using multiple sensors of multiple modalities -thereby using sensor fusion for situational awareness. We show the construction of HMM and apply it to the data collected using a suite of sensors on a Packbot.

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“…Also, a device for the observation process is not necessarily limited to a vision device. Bernardin et al [2] used sensor gloves for obtaining a shape of grasping hands. As shown in these studies, a device and a technique appropriate for target tasks are required for the observation process.…”

Section: Observation Process In Lfomentioning

confidence: 99%

Learning from Observation Paradigm: Leg Task Models for Enabling a Biped Humanoid Robot to Imitate Human Dances

Nakaoka

Nakazawa

Kanehiro

et al. 2007

The International Journal of Robotics Research

131

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This thesis proposes a framework that enables a biped humanoid robot to learn and reproduce whole body motions from human motions. This ability can increase the usefulness of biped humanoid robots, which have a potential to perform human-like motions of the whole body. We achieve this goal on the basis of the Learning from Observation (LFO) paradigm, and prove that the paradigm is valid for whole body motions. In our framework, the target motions are dances, and a robot uses its own legs to support its body during a dance performance. Reproducing such motions from human motions is a novel attempt. This thesis especially focuses on leg motions, which present a serious challenge to the achievement of our goal.LFO is a paradigm of teaching a task to a robot. It consists of three processes: observation, recognition, and execution. First, a human instructor demonstrates a task and a robot observes the demonstration. Then the robot recognizes what is done in the observed motion. After that, the robot can execute the task according to the recognition results. In this paradigm, all an instructor has to do is to demonstrate a task with his or her body. This makes teaching simple and efficient because complex operations or programming by a robotic specialist is not required. This simultaneously means that a robot imitates an instructor's way of doing a task. These features are great advantages in teaching human-like motions to a robot that has a complex humanlike body.In the LFO paradigm, the recognition and execution processes are based on task models designed for a target task domain. Task models provide essential representations of tasks, which are necessary for executing tasks in various situations. One of the fundamental factors in different situations is the differences among bodies. In this study, to solve the problem caused by body difference is the most important role of task models.In general, human or humanoid bodies have their own physical characteristics different from each other. The differences include geometry of body parts, weight distribution, and physical capabilities. In addition, humanoid robots can have their own body mechanisms quite different from those of another body. Due to these differences, observed motions of an instructor cannot be performed directly by another body of a robot. It is hence inevitable that the motions are partly modified so that they are adapted to the body that executes the motions. This adaptation must be processed so that the original purpose of a task is achieved. For motions of the whole body including legs, body differences significantly affect the execution of motions because the motions are performed under severe constraints caused by the interaction with the floor. For motions such as dances, important characteristics of the original motions must be preserved simultaneously. These factors make reproducing whole body motions performed by another body a difficult problem. Since legs play a major role in the i interaction with the floor, leg motion is especially...

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A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models

Cited by 113 publications

References 14 publications

Human-like action segmentation for option learning

Human-like action segmentation for option learning

Hidden Markov Model as a Framework for Situational Awareness

Learning from Observation Paradigm: Leg Task Models for Enabling a Biped Humanoid Robot to Imitate Human Dances

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