ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes (2011.04812v2)

Abstract: 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 utility function over a region of interest that ensures safety and comfort. ROIAL learns from ordinal and preference feedback, which are more reliable feedback mechanisms than absolute numerical scores. The algorithm's performance is evaluated both in simulation and experimentally for three non-disabled subjects walking inside of a lower-body exoskeleton. ROIAL learns Bayesian posteriors that predict each exoskeleton user's utility landscape across four exoskeleton gait parameters. The algorithm discovers both commonalities and discrepancies across users' gait preferences and identifies the gait parameters that most influenced user feedback. These results demonstrate the feasibility of recovering gait utility landscapes from limited human trials.

• The paper introduces ROIAL, demonstrating how active learning and ordinal feedback can efficiently estimate personalized gait preference landscapes.
• It employs Gaussian processes and pairwise preference elicitation to minimize exploration of suboptimal gaits while prioritizing safety and comfort.
• Empirical trials reveal distinct preference contours among users, underscoring its potential for customizable exoskeleton gait optimization.

Region of Interest Active Learning for Exoskeleton Gait Preferences

The paper introduces the Region of Interest Active Learning (ROIAL) framework, a methodological advance aimed at estimating personalized utility landscapes for exoskeleton gait parameters. With the increasing development and usage of lower-body exoskeletons, understanding user preferences is crucial for optimizing comfort and safety, areas which still face substantive challenges due to complex gait dynamics and the limited availability of data from human trials.

Core Contributions and Methodology

ROIAL emerges to fill a gap in the current methodologies which focus predominantly on direct optimization for individualized exoskeleton gait without broader understanding of the preference landscapes. It adapts active learning techniques to focus on a region of interest (ROI), defined as the set of parameters that align with user safety and comfort while circumventing undesirable gaits categorized as the Region of Avoidance (ROA).

The framework differentiates itself by utilizing ordinal feedback and pairwise preference elicitation rather than relying exclusively on quantitative scores. This approach is especially pertinent given the reliability of qualitative feedback in capturing user sentiment, as the author's base Gaussian processes was shown to predict utility landscapes efficiently by leveraging this type of feedback. This is materialized through the maximization of information gain in selecting which gait parameters to test, an optimization that seeks to minimize exploration in suboptimal and potentially adverse regions.

Simulation and Experimental Validation

The effectiveness of ROIAL is substantiated through a multi-pronged evaluation strategy involving both simulations and experimental assessments with human participants. In simulated environments, the framework demonstrates significant efficacy, maintaining a high accuracy in learning user preferences within a constrained number of trials. Critically, ordinal label predictions and preference elicitation result in robust learning even in the face of limited noisy feedback, underscoring the algorithm's robustness.

Empirical trials with non-disabled participants utilizing the Atalante exoskeleton reveal key insights into personalized gait preferences. The outcomes illustrate the variance in gait preference landscapes across individuals, with experienced users showing distinct utility contours compared to inexperienced users. The parameter importance across subjects, such as step duration and pelvis motions, offers a nuanced understanding of the gait configurations that significantly impact user satisfaction and comfort. This confirms ROIAL’s utility in capturing personalized preferences, providing a path towards improved exoskeleton gait customization.

Significance and Future Directions

The introduction of ROIAL marks a meaningful step forward in preference-based learning strategies within high-dimensional action spaces like exoskeleton gait design. Its potential extends beyond solely improving personalized rehabilitation devices; it provides a foundational framework for other domains requiring preference learning amidst constrained, complex environments.

Future work should delve into adaptive noise modeling to better quantify and mitigate user biases in ordinal feedback. Extensive validation with a larger and more diverse participant pool, including individuals with motor impairments, is imperative for drawing conclusions applicable to clinical and practical scenarios. Moreover, continued refinement through iterative experimentation will ascertain ROIAL’s applicability across other contexts where human comfort and experience intersect with high-dimensional control tasks.

In closing, the ROIAL framework offers a forward-looking path in the field of exoskeleton research, encouraging ongoing exploration into the intricate dynamics of personalized human-technology interaction paradigms.