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Probabilistic Multimodal Modeling For Human-Robot Interaction Tasks.pdf

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Preview of Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks
🔗 Source: roboticsproceedings.org
📊 Size: 4.51 MB
📄 Pages: 9 pages
⬇️ Downloads: 319

Summary

Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks: This work introduces a reformulation of Interaction Primitives, called ensemble Bayesian Interaction Primitives (eBIP), for human-robot interaction scenarios, allowing for learning from demonstration and handling nonlinearities in multimodal inference. eBIP combines advantages of parametric and non-parametric methods, avoids linearization errors, and enables fast and efficient inference in high-dimensional spaces. Experiments show that eBIP outperforms other methods in a fast-paced, dynamic human-robot interaction experiment involving multimodal sensor streams.

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Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks: This work introduces a reformulation of Interaction Primitives, called ensemble Bayesian...

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  • File Format: PDF
  • File Size: 4.51 MB
  • Pages: 9
  • Language: EN
  • Total Downloads: 319
  • Last Updated: 2 weeks ago

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