# Utilizing Human Feedback for Primitive Optimization in Wheelchair Tennis

arXiv: https://arxiv.org/abs/2212.14403 · HTML: https://mlsanity.com/paper/2212.14403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2022.

## Built on by (most cited first, 1 of 1)

- [Learning Diverse Robot Striking Motions with Diffusion Models and Kinematically Constrained Gradient Guidance](https://mlsanity.com/paper/2409.15528/) (2024)

## Abstract (arXiv)

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.