Teaching Human Teachers to Teach Robot Learners
Open accessAbstract
Using Programming by Demonstration to teach robot learners generalisable skills relies on having effective human teachers. This paper aims to address two problems commonly observed in demonstration data sets that arise due to poor teaching strategies; undemonstrated states and ambiguous demonstrations. Overcoming these issues through the use of visual feedback and simple heuristic rules is investigated as a potential way of guiding novice users to more effectively teach robot learners to generalise a task. The proposed method intends to offer the user a more transparent understanding of the robot learner's model state during the teaching phase, to create a more interactive and robust teaching process. Results from a single-factor, three-phase repeated measures study with n=30 participants, comparing the proposed feedback and heuristic rules set against an unguided condition, show a statistically significant (F(2,58)=7.952,p=0.001) improvement of user teaching efficiency of approximately 180% when using the proposed feedback visualisation.
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The magnitude of the covariance matrix, evaluated by its determinant, is considered an indicator for whether a defined task parameter is required to learn a task.
“Here, the magnitude of the covariance matrix, evaluated using a matrix determinant, is considered an indicator for this.”
From Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation (2019) · cites this paper as [8]
The Task Learning section describes task-parameterised Gaussian mixture modelling and its reference frames, but does not describe using a covariance-matrix determinant to decide whether a task parameter is required. Searches for covariance-matrix magnitude and determinant returned no passage addressing that criterion; the supplied passages have no page numbers. (LLM, conf=0.74)
Experimental — accuracy not yet measuredThe paper builds on confidence weighting and frame importance sampling to present an autonomous method for weighting task frames during regression to improve extrapolation while retaining local structure.
“To address these issues, this paper builds on the confidence weighting scheme presented in [7] and the frame importance sampling in [8], and presents an autonomous method for weighting task frames during regression to improve model extrapolation, while retaining local structure observed in demonstration data.”
From Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation (2019) · cites this paper as [8]
The citing sentence presents the autonomous regression method as the citing authors’ own contribution, not necessarily as a contribution of this source. In Task Learning and Trajectory Generation (pages unavailable), our copy describes TP-GMM/GMR and distance-weighted estimates of initiation-frame transformations, but does not describe a confidence-weighting scheme or frame-importance sampling. Searches for those terms and for weighting task frames during regression found no supporting passage. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Cites
Statistical power analyses using G∗Power 3.1: tests for correlation and regression analyses
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A survey of robot learning from demonstration
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On Learning, Representing, and Generalizing a Task in a Humanoid Robot
Calinon, S., Guenter, F., & Billard, A. (2007) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2006.886952 · Cited by 1,090 · see citing works
Robot Programming by Demonstration
Billard, A., Calinon, S., Dillmann, R., & Schaal, S. (2008) · Springer eBooks · doi:10.1007/978-3-540-30301-5_60 · Cited by 1,078 · see citing works
Dynamic Movement Primitives -A Framework for Motor Control in Humans and Humanoid Robotics
Schaal, S. (2006) · Springer eBooks · doi:10.1007/4-431-31381-8_23 · Cited by 620 · see citing works
A tutorial on task-parameterized movement learning and retrieval
Calinon, S. (2016) · Intelligent Service Robotics · doi:10.1007/s11370-015-0187-9 · Cited by 422 · cited 2× · see citing works
“Each user-provided demonstration, Dm, contains Tm data points which are then concatenated to form a data set, D, of N points, with N = ∑M 1 Tm [13].”
BackgroundMethodHow Do Humans Teach: On Curriculum Learning and Teaching Dimension
Khan, F. S., Mutlu, B., & Zhu, X. (2011) · Cited by 121 · see citing works
What is the teacher’s role in robot programming by demonstration?
Calinon, S., & Billard, A. (2007) · Interaction Studies Social Behaviour and Communication in Biological and Artificial Systems · doi:10.1075/is.8.3.08cal · Cited by 117 · see citing works
Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM
Calinon, S., & Billard, A. G. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102365 · Cited by 104 · see citing works
Robot Programming by Demonstration with Interactive Action Visualizations
Alexandrova, S., Çakmak, M., Hsiao, K., & Takayama, L. A. (2014) · Robotics: Science and Systems · doi:10.15607/rss.2014.x.048 · Cited by 88 · see citing works
Learning task-parameterized dynamic movement primitives using mixture of GMMs
Pervez, A., & Lee, D. (2017) · Intelligent Service Robotics · doi:10.1007/s11370-017-0235-8 · Cited by 75 · see citing works
Eliciting good teaching from humans for machine learners
Çakmak, M., & Thomaz, A. L. (2014) · Artificial Intelligence · doi:10.1016/j.artint.2014.08.005 · Cited by 52 · see citing works
Active Incremental Learning of Robot Movement Primitives
(2017) · HAL · Cited by 42 · see citing works
Optimality of human teachers for robot learners
Çakmak, M., & Thomaz, A. L. (2010) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2010.5578865 · Cited by 29 · see citing works
A practical comparison of three robot learning from demonstration algorithms
Toris, R., Suay, H. B., & Chernova, S. (2012) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/2157689.2157784 · Cited by 18 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- A. Billard, S. Calinon, R. Dillmann, and S. Schaal, "Robot Program- ming by Demonstration," in Springer Handbook of Robotics. Berlin, Heidelberg: Springer, 2008, pp. 1371-1394.
- M. Cakmak and A. L. Thomaz, "Active Learning with Mixed Query Types in Learning from Demonstration," in Proceedings of the ICML Workshop on New Developments in Imitation Learning, 2011.
Cited by
Sorted by citations
Sena, A., Michael, B., & Howard, M. (2019) · 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967688 · Cited by 26 · see citing works
“Furthermore, the person teaching is fallible and prone to poor teaching behaviours such as not being able to gauge the appropriate number of demonstrations required for a robot to learn a task and struggling to identify gaps in the learners knowledge [1], [3].”
BackgroundSelf-citation
- Authors
- Sena, A., Zhao, Y., & Howard, M. J.
- Affiliations
- King's College London (GB)
- Venue
- 2018 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2018-05
- Pages
- 5675-5681
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2883304593
- Cited by
- 15
- References
- 15
- Citations by year
- 2025: 1 · 2022: 5 · 2021: 4 · 2020: 1 · 2019: 4
- FWCI
- 3.38
- Type
- conference paper
- Language
- en
- Open access
- green
- kclpure.kcl.ac.uk
- Licence
- other-oa
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- programming by demonstration, robot learning, human-robot teaching, visual feedback, heuristic rules, teaching efficiency, human-robot interaction
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