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

Authors:
K. Bernardin;K. Ogawara;K. Ikeuchi;R. Dillmann
Affiliations:
Inst. fuer Logik, Univ. Karlsruhe, Germany;-;-;-
Venue:
IEEE Transactions on Robotics
Year:
2005

Citing 0
Cited 18

Recognition of in-hand manipulation using contact state transition for multifingered robot hand control

Robotics and Autonomous Systems
Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains

International Journal of Robotics Research
On Signature Invariants for Effective Motion Trajectory Recognition

International Journal of Robotics Research
Dynamic Grasp Recognition Using Time Clustering, Gaussian Mixture Models and Hidden Markov Models

ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
A Skill-Based Autonomous Control Method for Robotic Teleoperation

ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
Recognition of human grasps by time-clustering and fuzzy modeling

Robotics and Autonomous Systems
Office activity recognition using hand posture cues

BCS-HCI '08 Proceedings of the 22nd British HCI Group Annual Conference on People and Computers: Culture, Creativity, Interaction - Volume 2
Incremental Learning and Memory Consolidation of Whole Body Human Motion Primitives

Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Synthesizing a desired trajectory and sensory feedback control laws for an origami-folding robot based on the statistical characteristics of direct teaching by a human

ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Detecting repeated motion patterns via dynamic programming using motion density

ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Online segmentation and clustering from continuous observation of whole body motions

IEEE Transactions on Robotics
Segmentation method of human manipulation task based on measurement of force imposed by a human hand on a grasped object

IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Learning Visual Object Categories for Robot Affordance Prediction

International Journal of Robotics Research
Object interaction detection using hand posture cues in an office setting

International Journal of Human-Computer Studies
Feature fusion for 3D hand gesture recognition by learning a shared hidden space

Pattern Recognition Letters
Templates for pre-grasp sliding interactions

Robotics and Autonomous Systems
Discriminative functional analysis of human movements

Pattern Recognition Letters
Context-based hand gesture recognition for the operating room

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 information obtained from a data glove and tactile sensors to recognize continuous human-grasp sequences. The sensor fusion, grasp classification, and task segmentation are made by a hidden Markov model recognizer. Twelve different grasp types from a general, task-independent taxonomy are recognized. An accuracy of up to 95% could be achieved for a multiple-user system.