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Hierarchical Task-Parameterized Learning from Demonstration for Collaborative Object Movement

2019

Article

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Learning from demonstration (LfD) enables a robot to emulate natural human movement instead of merely executing preprogrammed behaviors. This article presents a hierarchical LfD structure of task-parameterized models for object movement tasks, which are ubiquitous in everyday life and could benefit from robotic support. Our approach uses the task-parameterized Gaussian mixture model (TP-GMM) algorithm to encode sets of demonstrations in separate models that each correspond to a different task situation. The robot then maximizes its expected performance in a new situation by either selecting a good existing model or requesting new demonstrations. Compared to a standard implementation that encodes all demonstrations together for all test situations, the proposed approach offers four advantages. First, a simply defined distance function can be used to estimate test performance by calculating the similarity between a test situation and the existing models. Second, the proposed approach can improve generalization, e.g., better satisfying the demonstrated task constraints and speeding up task execution. Third, because the hierarchical structure encodes each demonstrated situation individually, a wider range of task situations can be modeled in the same framework without deteriorating performance. Last, adding or removing demonstrations incurs low computational load, and thus, the robot’s skill library can be built incrementally. We first instantiate the proposed approach in a simulated task to validate these advantages. We then show that the advantages transfer to real hardware for a task where naive participants collaborated with a Willow Garage PR2 robot to move a handheld object. For most tested scenarios, our hierarchical method achieved significantly better task performance and subjective ratings than both a passive model with only gravity compensation and a single TP-GMM encoding all demonstrations.

Author(s): Siyao Hu and Katherine J. Kuchenbecker
Journal: Applied Bionics and Biomechanics
Number (issue): 9765383
Year: 2019
Month: December

Department(s): Haptic Intelligence
Bibtex Type: Article (article)
Paper Type: Journal

DOI: 10.1155/2019/9765383

BibTex

@article{Hu19-ABB-Hierarchical,
  title = {Hierarchical Task-Parameterized Learning from Demonstration for Collaborative Object Movement},
  author = {Hu, Siyao and Kuchenbecker, Katherine J.},
  journal = {Applied Bionics and Biomechanics},
  number = {9765383},
  month = dec,
  year = {2019},
  month_numeric = {12}
}