ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes

@article{Li2021ROIALRO,
  title={ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes},
  author={Kejun Li and Maegan Tucker and Erdem Biyik and Ellen R. Novoseller and Joel W. Burdick and Yanan Sui and Dorsa Sadigh and Yisong Yue and A. Ames},
  journal={2021 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2021},
  pages={3212-3218}
}
  • Kejun Li, Maegan Tucker, A. Ames
  • Published 9 November 2020
  • Computer Science
  • 2021 IEEE International Conference on Robotics and Automation (ICRA)
Characterizing what types of exoskeleton gaits are comfortable for users, and understanding the science of walking more generally, require recovering a user’s utility landscape. Learning these landscapes is challenging, as walking trajectories are defined by numerous gait parameters, data collection from human trials is expensive, and user safety and comfort must be ensured. This work proposes the Region of Interest Active Learning (ROIAL) framework, which actively learns each user’s underlying… 

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References

SHOWING 1-10 OF 36 REFERENCES
Preference-Based Learning for Exoskeleton Gait Optimization
TLDR
In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton’s walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users.
Preference-based Online Learning with Dueling Bandits: A Survey
TLDR
The aim of this paper is to provide a survey of the state of the art in this field, referred to as preference-based multi-armed bandits or dueling bandits, and to provide an overview of problems that have been considered in the literature as well as methods for tackling them.
Active Preference Learning using Maximum Regret
TLDR
This work proposes a query selection that greedily reduces the maximum error ratio over the solution space and demonstrates that the proposed approach outperforms other state of the art techniques in both learning efficiency and ease of queries for the user.
Active Preference-Based Gaussian Process Regression for Reward Learning
TLDR
This work model the reward function using a Gaussian Process and proposes a mathematical formulation to actively find a GP using only human preferences, enabling it to tackle both inflexibility and data-inefficiency problems within a preference-based learning framework.
Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
TLDR
This work empirically verify that LINECOSPAR is a sample-efficient approach for high-dimensional preference optimization and analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamicity, while also highlighting differences in the utility functions underlying individual users’ gait preferences.
Asking Easy Questions: A User-Friendly Approach to Active Reward Learning
TLDR
This paper explores an information gain formulation for optimally selecting questions that naturally account for the human's ability to answer, and determines when these questions become redundant or costly.
Personalized gait trajectory generation based on anthropometric features using Random Forest
TLDR
A personalized gait generation method based anthropometric features is proposed and the performance of the proposed method is demonstrated by several comparison experiments.
A Method for Online Optimization of Lower Limb Assistive Devices with High Dimensional Parameter Spaces
TLDR
The offline portion of the optimization method indeed produces control parameters that can adapt to different gaits, and for three out of the four parameter sets the authors tested, the procedure also generates parameters that improve the ability of the prosthesis to adapt to increasing gait speed by increasing ankle net work production.
Dynamic Humanoid Locomotion: A Scalable Formulation for HZD Gait Optimization
TLDR
A methodology that allows for fast and reliable generation of dynamic robotic walking gaits through the HZD framework, even in the presence of underactuation, and develops a defect-variable substitution formulation to simplify expressions, which ultimately allows for compact analytic Jacobians of the constraints.
Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable, Hands-Free Dynamic Walking
TLDR
This article describes the hardware that was designed to achieve hands-free dynamic walking, the control laws that were deployed (and those being developed) to provide enhanced mobility and robustness, and preliminary test results.
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