21 research outputs found
An Input-to-State Stability Perspective on Robust Locomotion
Uneven terrain necessarily transforms periodic walking into a non-periodic
motion. As such, traditional stability analysis tools no longer adequately
capture the ability of a bipedal robot to locomote in the presence of such
disturbances. This motivates the need for analytical tools aimed at generalized
notions of stability -- robustness. Towards this, we propose a novel definition
of robustness, termed \emph{-robustness}, to characterize the domain on
which a nominal periodic orbit remains stable despite uncertain terrain. This
definition is derived by treating perturbations in ground height as
disturbances in the context of the input-to-state-stability (ISS) of the
extended Poincar\'{e} map associated with a periodic orbit. The main theoretic
result is the formulation of robust Lyapunov functions that certify
-robustness of periodic orbits. This yields an optimization framework
for verifying -robustness, which is demonstrated in simulation with a
bipedal robot walking on uneven terrain.Comment: 6 page
Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion
Successfully achieving bipedal locomotion remains challenging due to
real-world factors such as model uncertainty, random disturbances, and
imperfect state estimation. In this work, we propose the use of discrete-time
barrier functions to certify hybrid forward invariance of reduced step-to-step
dynamics. The size of these invariant sets can then be used as a metric for
locomotive robustness. We demonstrate an application of this metric towards
synthesizing robust nominal walking gaits using a simulation-in-the-loop
approach. This procedure produces reference motions with step-to-step dynamics
that are maximally forward-invariant with respect to the reduced representation
of choice. The results demonstrate robust locomotion for both flat-foot walking
and multi-contact walking on the Atalante lower-body exoskeleton
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
Humanoid Robot Co-Design: Coupling Hardware Design with Gait Generation via Hybrid Zero Dynamics
Selecting robot design parameters can be challenging since these parameters
are often coupled with the performance of the controller and, therefore, the
resulting capabilities of the robot. This leads to a time-consuming and often
expensive process whereby one iterates between designing the robot and manually
evaluating its capabilities. This is particularly challenging for bipedal
robots, where it can be difficult to evaluate the behavior of the system due to
the underlying nonlinear and hybrid dynamics. Thus, in an effort to streamline
the design process of bipedal robots, and maximize their performance, this
paper presents a systematic framework for the co-design of humanoid robots and
their associated walking gaits. To this end, we leverage the framework of
hybrid zero dynamic (HZD) gait generation, which gives a formal approach to the
generation of dynamic walking gaits. The key novelty of this paper is to
consider both virtual constraints associated with the actuators of the robot,
coupled with design virtual constraints that encode the associated parameters
of the robot to be designed. These virtual constraints are combined in an HZD
optimization problem which simultaneously determines the design parameters
while finding a stable walking gait that minimizes a given cost function. The
proposed approach is demonstrated through the design of a novel humanoid robot,
ADAM, wherein its thigh and shin are co-designed so as to yield energy
efficient bipedal locomotion.Comment: 7 pages, 6 figures, accepted to CDC 202
Towards Variable Assistance for Lower Body Exoskeletons
This letter presents and experimentally demonstrates a novel framework for variable assistance on lower body exoskeletons, based upon safety-critical control methods. Existing work has shown that providing some freedom of movement around a nominal gait, instead of rigidly following it, accelerates the spinal learning process of people with a walking impediment when using a lower body exoskeleton. With this as motivation, we present a method to accurately control how much a subject is allowed to deviate from a given gait while ensuring robustness to patient perturbation. This method leverages control barrier functions to force certain joints to remain inside predefined trajectory tubes in a minimally invasive way. The effectiveness of the method is demonstrated experimentally with able-bodied subjects and the Atalante lower body exoskeleton
Stabilization of Exoskeletons through Active Ankle Compensation
This paper presents an active stabilization method for a fully actuated lower-limb exoskeleton. The method was tested on the exoskeleton ATALANTE, which was designed and built by the French start-up company Wandercraft. The main objective of this paper is to present a practical method of realizing more robust walking on hardware through active ankle compensation. The nominal gait was generated through the hybrid zero dynamic framework. The ankles are individually controlled to establish three main directives; (1) keeping the non-stance foot parallel to the ground, (2) maintaining rigid contact between the stance foot and the ground, and (3) closing the loop on pelvis orientation to achieve better tracking. Each individual component of this method was demonstrated separately to show each component's contribution to stability. The results showed that the ankle controller was able to experimentally maintain static balance in the sagittal plane while the exoskeleton was balanced on one leg, even when disturbed. The entire ankle controller was then also demonstrated on crutch-less dynamic walking. During testing, an anatomically correct manikin was placed in the exoskeleton, in lieu of a paraplegic patient. The pitch of the pelvis of the exoskeleton-manikin system was shown to track the gait trajectory better when ankle compensation was used. Overall, active ankle compensation was demonstrated experimentally to improve balance in the sagittal plane of the exoskeleton manikin system and points to an improved practical approach for stable walking