2011 IEEE/RSJ International Conference on Intelligent Robots and Systems 2011
DOI: 10.1109/iros.2011.6095026
|View full text |Cite
|
Sign up to set email alerts
|

An experience-driven robotic assistant acquiring human knowledge to improve haptic cooperation

Abstract: Abstract-Physical cooperation with humans greatly enhances the capabilities of robotic systems when leaving standardized industrial settings. Our novel cognition-enabled control framework presented in this paper enables a robotic assistant to enrich its own experience by acquisition of human task knowledge during joint manipulation. Our robot incrementally learns semantic task structures during joint task execution using hierarchically clustered Hidden Markov Models. A semantic labeling of recognized task segm… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2013
2013
2022
2022

Publication Types

Select...
5
2

Relationship

1
6

Authors

Journals

citations
Cited by 64 publications
(3 citation statements)
references
References 16 publications
0
3
0
Order By: Relevance
“…The control community uses adaptive control and iterative learning control to estimate x * h and adapt x * [105][106][107][108][109][110], while the ML community uses probabilistic models to encode x * h and decode it for adaptation of x * [57,60,111,112]. These two approaches may have complementary advantages: the modelling in control theory has more intuitive physical meanings and the system performance can be analysed in rigour; while the ML method takes system uncertainties into account, which are inevitable in a human-robot interaction system.…”
Section: Assistancementioning
confidence: 99%
See 1 more Smart Citation
“…The control community uses adaptive control and iterative learning control to estimate x * h and adapt x * [105][106][107][108][109][110], while the ML community uses probabilistic models to encode x * h and decode it for adaptation of x * [57,60,111,112]. These two approaches may have complementary advantages: the modelling in control theory has more intuitive physical meanings and the system performance can be analysed in rigour; while the ML method takes system uncertainties into account, which are inevitable in a human-robot interaction system.…”
Section: Assistancementioning
confidence: 99%
“…[ [105][106][107][108][109][110]: same as human motion target, adaptive/iterative learning control [57,60,111,112]: probabilistic models [47]: interdependent motion targets [47]: independent motion targets [47,102,103]: opposite motion target Robot control command (ξ 0 = u0, ξ h = u h , ξ = u) [118,119,121,122]: minimization of human control command [100,[115][116][117]: supplement to human control command (u0 = 0) [118,119,121,122]: interdependent control commands (u0 ̸ = 0) [118,119,121,122]: independent control commands Robot gain (ξ 0 = K0,…”
Section: Assistancementioning
confidence: 99%
“…In Rozo et al (2015), Gaussian functions are used to classify and learn cooperative human-robot skills in the context of object transport. In Medina et al (2011), a method is proposed using Markov models to increase the experience of a manipulator robot in collaborative tasks with humans; the control adapts and improves cooperation through user speech commands and repetitive haptic training tasks.…”
Section: Overview Of Related Workmentioning
confidence: 99%