Human–exoskeleton interactions have the potential to bring about changes in human behavior for physical rehabilitation or skill augmentation. Despite significant advances in the design and control of these robots, their application to human training remains limited. The key obstacles to the design of such training paradigms are the prediction of human–exoskeleton interaction effects and the selection of interaction control to affect human behavior. In this article, we present a method to elucidate behavioral changes in the human–exoskeleton system and identify expert behaviors correlated with a task goal. Specifically, we observe the joint coordinations of the robot, also referred to as kinematic coordination behaviors, that emerge from human–exoskeleton interaction during learning. We demonstrate the use of kinematic coordination behaviors with two task domains through a set of three human-subject studies. We find that participants (1) learn novel tasks within the exoskeleton environment, (2) demonstrate similarity of coordination during successful movements within participants, (3) learn to leverage these coordination behaviors to maximize success within participants, and (4) tend to converge to similar coordinations for a given task strategy across participants. At a high level, we identify task-specific joint coordinations that are used by different experts for a given task goal. These coordinations can be quantified by observing experts and the similarity to these coordinations can act as a measure of learning over the course of training for novices. The observed expert coordinations may further be used in the design of adaptive robot interactions aimed at teaching a participant the expert behaviors.
A Novel Control Law for Multi-joint Human-Robot Interaction Tasks While Maintaining Postural Coordination
Exoskeleton robots are capable of safe torque- controlled interactions with a wearer while moving their limbs through pre-defined trajectories. However, affecting and assist- ing the wearer’s movements while incorporating their inputs (effort and movements) effectively during an interaction re- mains an open problem due to the complex and variable nature of human motion. In this paper, we present a control algorithm that leverages task-specific movement behaviors to control robot torques during unstructured interactions by implementing a force field that imposes a desired joint angle coordination behavior. This control law, built by using principal component analysis (PCA), is implemented and tested with the Harmony exoskeleton. We show that the proposed control law is versatile enough to allow for the imposition of different coordination behaviors with varying levels of impedance stiffness. We also test the feasibility of our method for unstructured human-robot interaction. Specifically, we demonstrate that participants in a human-subject experiment are able to effectively perform reaching tasks while the exoskeleton imposes the desired joint coordination under different movement speeds and interaction modes. Survey results further suggest that the proposed control law may offer a reduction in cognitive or motor effort. This control law opens up the possibility of using the exoskeleton for training the participating in accomplishing complex m
more »
« less
- Award ID(s):
- 2019704
- NSF-PAR ID:
- 10469306
- Publisher / Repository:
- IEEE
- Date Published:
- ISSN:
- 2767-9470
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
Healthy human locomotion functions with good gait symmetry depend on rhythmic coordination of the left and right legs, which can be deteriorated by neurological disorders like stroke and spinal cord injury. Powered exoskeletons are promising devices to improve impaired people's locomotion functions, like gait symmetry. However, given higher uncertainties and the time-varying nature of human-robot interaction, providing personalized robotic assistance from exoskeletons to achieve the best gait symmetry is challenging, especially for people with neurological disorders. In this paper, we propose a hierarchical control framework for a bilateral hip exoskeleton to provide the adaptive optimal hip joint assistance with a control objective of imposing the desired gait symmetry during walking. Three control levels are included in the hierarchical framework, including the high-level control to tune three control parameters based on a policy iteration reinforcement learning approach, the middle-level control to define the desired assistive torque profile based on a delayed output feedback control method, and the low-level control to achieve a good torque trajectory tracking performance. To evaluate the feasibility of the proposed control framework, five healthy young participants are recruited for treadmill walking experiments, where an artificial gait asymmetry is imitated as the hemiparesis post-stroke, and only the ‘paretic’ hip joint is controlled with the proposed framework. The pilot experimental studies demonstrate that the hierarchical control framework for the hip exoskeleton successfully (asymmetry index from 8.8% to − 0.5%) and efficiently (less than 4 minutes) achieved the desired gait symmetry by providing adaptive optimal assistance on the ‘paretic’ hip joint.more » « less
-
null (Ed.)Over the past few decades, there have been many studies of human-human physical interaction to better understand why humans physically interact so effectively and how dyads outperform individuals in certain motor tasks. Because of the different methodologies and experimental setups in these studies, however, it is difficult to draw general conclusions as to the reasons for this improved performance. In this study, we propose an open-source experimental framework for the systematic study of the effect of human-human interaction, as mediated by robots, at the ankle joint. We also propose a new framework to study various interactive behaviors (i.e., collaborative, cooperative, and competitive tasks) that can be emulated using a virtual spring connecting human pairs. To validate the proposed experimental framework, we perform a transparency analysis, which is closely related to haptic rendering performance. We compare muscle EMG and ankle motion data while subjects are barefoot, attached to the unpowered robot, and attached to the powered robot implementing transparency control. We also validate the performance in rendering a virtual springs covering a range of stiffness values (5-50 Nm/rad) while the subjects track several desired trajectories(sine waves at frequencies between 0.1 and 1.1 Hz). Finally, we study the performance of the system in human-human interaction under nine different interactive conditions. Finally, we demonstrate the feasibility of the system in studying human-human interaction under different interactive behaviors.more » « less
-
Abstract Background Few studies have systematically investigated robust controllers for lower limb rehabilitation exoskeletons (LLREs) that can safely and effectively assist users with a variety of neuromuscular disorders to walk with full autonomy. One of the key challenges for developing such a robust controller is to handle different degrees of uncertain human-exoskeleton interaction forces from the patients. Consequently, conventional walking controllers either are patient-condition specific or involve tuning of many control parameters, which could behave unreliably and even fail to maintain balance. Methods We present a novel, deep neural network, reinforcement learning-based robust controller for a LLRE based on a decoupled offline human-exoskeleton simulation training with three independent networks, which aims to provide reliable walking assistance against various and uncertain human-exoskeleton interaction forces. The exoskeleton controller is driven by a neural network control policy that acts on a stream of the LLRE’s proprioceptive signals, including joint kinematic states, and subsequently predicts real-time position control targets for the actuated joints. To handle uncertain human interaction forces, the control policy is trained intentionally with an integrated human musculoskeletal model and realistic human-exoskeleton interaction forces. Two other neural networks are connected with the control policy network to predict the interaction forces and muscle coordination. To further increase the robustness of the control policy to different human conditions, we employ domain randomization during training that includes not only randomization of exoskeleton dynamics properties but, more importantly, randomization of human muscle strength to simulate the variability of the patient’s disability. Through this decoupled deep reinforcement learning framework, the trained controller of LLREs is able to provide reliable walking assistance to patients with different degrees of neuromuscular disorders without any control parameter tuning. Results and conclusion A universal, RL-based walking controller is trained and virtually tested on a LLRE system to verify its effectiveness and robustness in assisting users with different disabilities such as passive muscles (quadriplegic), muscle weakness, or hemiplegic conditions without any control parameter tuning. Analysis of the RMSE for joint tracking, CoP-based stability, and gait symmetry shows the effectiveness of the controller. An ablation study also demonstrates the strong robustness of the control policy under large exoskeleton dynamic property ranges and various human-exoskeleton interaction forces. The decoupled network structure allows us to isolate the LLRE control policy network for testing and sim-to-real transfer since it uses only proprioception information of the LLRE (joint sensory state) as the input. Furthermore, the controller is shown to be able to handle different patient conditions without the need for patient-specific control parameter tuning.more » « less
-
High-performance actuators are crucial to enable mechanical versatility of wearable robots, which are required to be lightweight, highly backdrivable, and with high bandwidth. State-of-the-art actuators, e.g., series elastic actuators (SEAs), have to compromise bandwidth to improve compliance (i.e., backdrivability). We describe the design and human-robot interaction modeling of a portable hip exoskeleton based on our custom quasi-direct drive (QDD) actuation (i.e., a high torque density motor with low ratio gear). We also present a model-based performance benchmark comparison of representative actuators in terms of torque capability, control bandwidth, backdrivability, and force tracking accuracy. This paper aims to corroborate the underlying philosophy of “design for control“, namely meticulous robot design can simplify control algorithms while ensuring high performance. Following this idea, we create a lightweight bilateral hip exoskeleton to reduce joint loadings during normal activities, including walking and squatting. Experiments indicate that the exoskeleton is able to produce high nominal torque (17.5 Nm), high backdrivability (0.4 Nm backdrive torque), high bandwidth (62.4 Hz), and high control accuracy (1.09 Nm root mean square tracking error, 5.4% of the desired peak torque). Its controller is versatile to assist walking at different speeds and squatting. This work demonstrates performance improvement compared with state-of-the-art exoskeletons.more » « less
