[2212.14403] Utilizing Human Feedback for Primitive Optimization in Wheelchair Tennis
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Computer Science > Robotics

arXiv:2212.14403 (cs)
[Submitted on 29 Dec 2022]

Title:Utilizing Human Feedback for Primitive Optimization in Wheelchair Tennis

Authors:Arjun Krishna, Zulfiqar Zaidi, Letian Chen, Rohan Paleja, Esmaeil Seraj, Matthew Gombolay
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Abstract:Agile robotics presents a difficult challenge with robots moving at high speeds requiring precise and low-latency sensing and control. Creating agile motion that accomplishes the task at hand while being safe to execute is a key requirement for agile robots to gain human trust. This requires designing new approaches that are flexible and maintain knowledge over world constraints. In this paper, we consider the problem of building a flexible and adaptive controller for a challenging agile mobile manipulation task of hitting ground strokes on a wheelchair tennis robot. We propose and evaluate an extension to work done on learning striking behaviors using a probabilistic movement primitive (ProMP) framework by (1) demonstrating the safe execution of learned primitives on an agile mobile manipulator setup, and (2) proposing an online primitive refinement procedure that utilizes evaluative feedback from humans on the executed trajectories.
Comments: Workshop paper at Learning for Agile Robotics Workshop, CoRL 2022
Subjects: Robotics (cs.RO)
Cite as: arXiv:2212.14403 [cs.RO]
  (or arXiv:2212.14403v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2212.14403

Submission history

From: Arjun Krishna [view email]
[v1] Thu, 29 Dec 2022 18:24:37 UTC (1,680 KB)
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