Preference-Based Learning for Exoskeleton Gait Optimization

This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. 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

Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users’ preferences over a high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LINECOSPAR, a human-in-the-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional subspaces. Additionally, this work identifies gait attributes that characterize broader preferences across users. In simulations and human trials, we empirically verify that LINECOSPAR is a sample-efficient approach for high-dimensional preference optimization. Our 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. This result has implications for exoskeleton gait synthesis, an active field with applications to clinical use and patient rehabilitation

Preference-Based Learning for Exoskeleton Gait Optimization

This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. 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

Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

Understanding users' gait preferences of a lower-body exoskeleton requires optimizing over the high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LineCoSpar, a human-in-the-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional subspaces. Additionally, this work identifies gait attributes that characterize broader preferences across users. In simulations and human trials, we empirically verify that LineCoSpar is a sample-efficient approach for high-dimensional preference optimization. Our analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamic stability, while also highlighting inconsistencies in the utility functions underlying different users' gait preferences. This has implications for exoskeleton gait synthesis, an active field with applications to clinical use and patient rehabilitation

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

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 able-bodied 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

Mechanisms of energy optimization in human walking

Humans can learn to move optimally. For many movements, we have a control strategy—or control policy—that optimizes some objective. In walking, we prefer the combination of step widths, step lengths, and speeds that optimizes the amount of energy we need. In familiar contexts, we have had many opportunities to establish this optimal control policy. But in new contexts, the nervous system must quickly learn new control policies in order to continue to move optimally. Our lab has recently demonstrated that humans can continuously optimize energetic cost during walking. This is an impressive feat given that the nervous system has tens of thousands of motor units at its disposal, and it can coordinate these motor units over millisecond timescales, which results in countless combinations of motor unit coordination. The goal of this thesis is to determine how the nervous system navigates this combinatorial problem to learn new energy optimal control policies in new walking contexts. I used three distinct studies to accomplish this goal. For the first two studies, I designed and implemented a simple mechatronic system that applies energetic penalties in the form of walking incline as a function of gait. This creates a new relationship between gait and energetic cost—or new cost landscape—that shifts the energy optimal gait. For the third study, I used exoskeletons that apply assistive torques to each ankle at each walking step to shift the energy optimal gait. The first study tested whether previous findings that people can learn to adapt their control policy when the energy optimum is shifted along step frequency generalize to a different gait parameter and to a different experimental setup. I found that, like step frequency, people can learn to adapt their control policy when the energy optimum is shifted along step width. The second study tested if and how energy optimization extends to multiple gait parameters at the same time. I found that, when the energy optimum is shifted along step width and step frequency, people are limited in their ability to optimize both gait parameters. The third study asked how people learn in which ways to optimize their policy. I found that general variability leads to specific adaptation toward optimal policies. Taken together, these findings provide insight into the mechanisms that underlie energy optimization in walking, as well as the limitations of this optimization

Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots

This paper presents a framework that unifies control theory and machine learning in the setting of bipedal locomotion. Traditionally, gaits are generated through trajectory optimization methods and then realized experimentally -- a process that often requires extensive tuning due to differences between the models and hardware. In this work, the process of gait realization via hybrid zero dynamics (HZD) based optimization problems is formally combined with preference-based learning to systematically realize dynamically stable walking. Importantly, this learning approach does not require a carefully constructed reward function, but instead utilizes human pairwise preferences. The power of the proposed approach is demonstrated through two experiments on a planar biped AMBER-3M: the first with rigid point feet, and the second with induced model uncertainty through the addition of springs where the added compliance was not accounted for in the gait generation or in the controller. In both experiments, the framework achieves stable, robust, efficient, and natural walking in fewer than 50 iterations with no reliance on a simulation environment. These results demonstrate a promising step in the unification of control theory and learning