Human intention estimation and goal-driven variable admittance control in manual guidance applications

Estimating and reshaping human intentions are among the most significant topics of research in the field of human-robot interaction. This chapter provides an overview of intention estimation literature on human-robot interaction, and introduces an approach on how robots can voluntarily reshape estimated intentions. The reshaping of the human intention is achieved by the robots moving in certain directions that have been a priori observed from the interactions of humans with the objects in the scene. Being among the only few studies on intention reshaping, the authors of this chapter exploit spatial information by learning a Hidden Markov Model (HMM) of motion, which is tailored for intelligent robotic interaction. The algorithmic design consists of two phases. At first, the approach detects and tracks human to estimate the current intention. Later, this information is used by autonomous robots that interact with detected human to change the estimated intention. In the tracking and intention estimation phase, postures and locations of the human are monitored by applying low-level video processing methods. In the latter phase, learned HMM models are used to reshape the estimated human intention. This two-phase system is tested on video frames taken from a real human-robot environment. The results obtained using the proposed approach shows promising performance in reshaping of detected intentions.

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Intention and Body-Mood Engineering via Proactive Robot Moves in HRI

Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-7278-9.ch012 ◽

2015 ◽

pp. 255-282 ◽

Cited By ~ 1

Author(s):

O. Can Görür ◽

Aydan M. Erkmen

Keyword(s):

Path Planning ◽

Mobile Robots ◽

Human Body ◽

Mood Induction ◽

Positive Mood ◽

Focused Attention ◽

Elastic Networks ◽

Human Intention ◽

Novel Concept ◽

Intention Estimation

This chapter focuses on emotion and intention engineering by socially interacting robots that induce desired emotions/intentions in humans. The authors provide all phases that pave this road, supported by overviews of leading works in the literature. The chapter is partitioned into intention estimation, human body-mood detection through external-focused attention, path planning through mood induction and reshaping intention. Moreover, the authors present their novel concept, with implementation, of reshaping current human intention into a desired one, using contextual motions of mobile robots. Current human intention has to be deviated towards the new desired one by destabilizing the obstinance of human intention, inducing positive mood and making the “robot gain curiosity of human”. Deviations are generated as sequences of transient intentions tracing intention trajectories. The authors use elastic networks to generate, in two modes of body mood: “confident” and “suspicious”, transient intentions directed towards the desired one, choosing among intentional robot moves previously learned by HMM.

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Human-Robot Collaboration using Variable Admittance Control and Human Intention Prediction

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) ◽

10.1109/case48305.2020.9217040 ◽

2020 ◽

Author(s):

Weifeng Lu ◽

Zhe Hu ◽

Jia Pan

Keyword(s):

Admittance Control ◽

Human Intention ◽

Human Robot Collaboration

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Human intention estimation algorithm design for robot in human and robot cooperated cell assembly

2010 International Symposium on Micro-NanoMechatronics and Human Science ◽

10.1109/mhs.2010.5669497 ◽

2010 ◽

Cited By ~ 1

Author(s):

Fei Chen ◽

Baiqing Sun ◽

Jian Huang ◽

Hironobu Sasaki ◽

Toshio Fukuda

Keyword(s):

Algorithm Design ◽

Estimation Algorithm ◽

Cell Assembly ◽

Human Intention ◽

Intention Estimation

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Robotic Hardware and Software Integration for Changing Human Intentions

Robotics ◽

10.4018/978-1-4666-4607-0.ch067 ◽

2013 ◽

pp. 1381-1406

Author(s):

Akif Durdu ◽

Ismet Erkmen ◽

Aydan M. Erkmen ◽

Alper Yilmaz

Keyword(s):

Video Processing ◽

Spatial Information ◽

Human Robot Interaction ◽

Phase System ◽

Robot Interaction ◽

Two Phase ◽

Video Frames ◽

Human Intention ◽

Two Phases ◽

Intention Estimation

Estimating and reshaping human intentions are among the most significant topics of research in the field of human-robot interaction. This chapter provides an overview of intention estimation literature on human-robot interaction, and introduces an approach on how robots can voluntarily reshape estimated intentions. The reshaping of the human intention is achieved by the robots moving in certain directions that have been a priori observed from the interactions of humans with the objects in the scene. Being among the only few studies on intention reshaping, the authors of this chapter exploit spatial information by learning a Hidden Markov Model (HMM) of motion, which is tailored for intelligent robotic interaction. The algorithmic design consists of two phases. At first, the approach detects and tracks human to estimate the current intention. Later, this information is used by autonomous robots that interact with detected human to change the estimated intention. In the tracking and intention estimation phase, postures and locations of the human are monitored by applying low-level video processing methods. In the latter phase, learned HMM models are used to reshape the estimated human intention. This two-phase system is tested on video frames taken from a real human-robot environment. The results obtained using the proposed approach shows promising performance in reshaping of detected intentions.

Download Full-text