Transition State Clustering for Interaction Segmentation and Learning (2402.14548v1)
Abstract: Hidden Markov Models with an underlying Mixture of Gaussian structure have proven effective in learning Human-Robot Interactions from demonstrations for various interactive tasks via Gaussian Mixture Regression. However, a mismatch occurs when segmenting the interaction using only the observed state of the human compared to the joint state of the human and the robot. To enhance this underlying segmentation and subsequently the predictive abilities of such Gaussian Mixture-based approaches, we take a hierarchical approach by learning an additional mixture distribution on the states at the transition boundary. This helps prevent misclassifications that usually occur in such states. We find that our framework improves the performance of the underlying Gaussian Mixture-based approach, which we evaluate on various interactive tasks such as handshaking and fistbumps.
- “Imitating by generating: Deep generative models for imitation of interactive tasks” In Frontiers in Robotics and AI 7 Frontiers Media SA, 2020, pp. 47
- Sylvain Calinon “A tutorial on task-parameterized movement learning and retrieval” In Intelligent service robotics 9 Springer, 2016, pp. 1–29
- “Learning collaborative manipulation tasks by demonstration using a haptic interface” In 2009 International Conference on Advanced Robotics, 2009, pp. 1–6 IEEE
- “Teaching physical collaborative tasks: object-lifting case study with a humanoid” In 2009 9th IEEE-RAS International Conference on Humanoid Robots, 2009, pp. 399–404 IEEE
- “Learning the k in k-means” In Advances in neural information processing systems 16, 2003
- “Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning” In The International journal of robotics research 36.13-14 SAGE Publications Sage UK: London, England, 2017, pp. 1595–1618
- Jangwon Lee “A survey of robot learning from demonstrations for human-robot collaboration” In arXiv preprint arXiv:1710.08789, 2017
- Masahiro Mori, Karl F MacDorman and Norri Kageki “The uncanny valley [from the field]” In IEEE Robotics & automation magazine 19.2 IEEE, 2012, pp. 98–100
- Deepika Phutela “The importance of non-verbal communication” In IUP Journal of Soft Skills 9.4 IUP Publications, 2015, pp. 43
- “Learning adaptive dressing assistance from human demonstration” In Robotics and Autonomous Systems 93, 2017, pp. 61–75 DOI: https://doi.org/10.1016/j.robot.2017.03.017
- “MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction” In 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids), 2022, pp. 472–479 DOI: 10.1109/Humanoids53995.2022.10000239
- L.R. Rabiner “A tutorial on hidden Markov models and selected applications in speech recognition” In Proceedings of the IEEE 77.2, 1989, pp. 257–286 DOI: 10.1109/5.18626
- “Learning physical collaborative robot behaviors from human demonstrations” In IEEE Transactions on Robotics 32.3 IEEE, 2016, pp. 513–527
- “Learning controllers for reactive and proactive behaviors in human–robot collaboration” In Frontiers in Robotics and AI 3 Frontiers Media SA, 2016, pp. 30
- “A system for learning continuous human-robot interactions from human-human demonstrations” In 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 2882–2889 IEEE
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