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@article{iros21_apple-wang,
  author = {Zizhao Wang and Xuesu Xiao and Bo Liu and Garrett Warnell and Peter Stone},
  title = {APPLE: Adaptive Planner Parameter Learning From Evaluative Feedback},
  journal = {{IEEE} Robotics and Automation Letters (RA-L)},
  month = {October},
  year = {2021},
  abstract = {
  Classical autonomous navigation systems can control robots in a 
  collision-free manner, oftentimes with verifiable safety and explainability. 
  When facing new environments, however, fine-tuning of the system parameters 
  by an expert is typically required before the system can navigate as 
  expected. To alleviate this requirement, the recently-proposed Adaptive 
  Planner Parameter Learning paradigm allows robots to learn how to 
  dynamically adjust planner parameters using a teleoperated demonstration or 
  corrective interventions from non-expert users. However, these 
  interaction modalities require users to take full control of the 
  moving robot, which requires the users to be familiar with robot 
  teleoperation. As an alternative, we introduce APPLE, Adaptive 
  Planner Parameter Learning from Evaluative Feedback (real-time, 
  scalar-valued assessments of behavior), which represents a less demanding 
  modality of interaction. Simulated and physical experiments show APPLE can 
  achieve better performance compared to the planner with static default 
  parameters and even yield improvement over learned parameters from richer 
  interaction modalities.
  },
  wwwnote={<a href="https://youtu.be/BSvrpU8Vv78">5-minute Video Presentation</a>;
  <a href="https://youtu.be/eKThRR7yCl4">15-minute Video Presentation</a>},
}