Virtual Physical Coupling of Two Lower-Limb Exoskeletons

Physical interaction between individuals plays an important role in human motor learning and performance during shared tasks. Using robotic devices, researchers have studied the effects of dyadic haptic interaction mostly focusing on the upper-limb. Developing infrastructure that enables physical interactions between multiple individuals' lower limbs can extend the previous work and facilitate investigation of new dyadic lower-limb rehabilitation schemes. We designed a system to render haptic interactions between two users while they walk in multi-joint lower-limb exoskeletons. Specifically, we developed an infrastructure where desired interaction torques are commanded to the individual lower-limb exoskeletons based on the users' kinematics and the properties of the virtual coupling. In this pilot study, we demonstrated the capacity of the platform to render different haptic properties (e.g., soft and hard), different haptic connection types (e.g., bidirectional and unidirectional), and connections expressed in joint space and in task space. With haptic connection, dyads generated synchronized movement, and the difference between joint angles decreased as the virtual stiffness increased. This is the first study where multi-joint dyadic haptic interactions are created between lower-limb exoskeletons. This platform will be used to investigate effects of haptic interaction on motor learning and task performance during walking, a complex and meaningful task for gait rehabilitation.

Haptic Transparency and Interaction Force Control for a Lower-Limb Exoskeleton

It is an open problem to control the interaction forces of lower-limb exoskeletons designed for unrestricted overground walking, i.e., floating base exoskeletons with feet that contact the ground. For these types of exoskeletons, it is challenging to measure interaction forces as it is not feasible to implement force/torque sensors at every contact between the user and the exoskeleton. Moreover, it is important to compensate for the exoskeleton's whole-body gravitational and dynamical forces. Previous works either simplified the dynamic model by treating the legs as independent double pendulums, or they did not close the loop with interaction force feedback. This paper presents a novel method to calculate interaction torques during the complete gait cycle by using whole-body dynamics and joint torque measurements on a hip-knee exoskeleton. Furthermore, we propose a constrained optimization scheme combined with a virtual model controller to track desired interaction torques in a closed loop while considering physical limits and safety considerations. Together, we call this approach whole-exoskeleton closed-loop compensation (WECC) control. We evaluated the haptic transparency and spring-damper rendering performance of WECC control on three subjects. We also compared the performance of WECC with a controller based on a simplified dynamic model and a passive version of the exoskeleton with disassembled drives. The WECC controller resulted in consistent interaction torque tracking during the whole gait cycle for both zero and nonzero desired interaction torques. On the contrary, the simplified controller failed to track desired interaction torques during the stance phase. The proposed interaction force control method is especially beneficial for heavy lower-limb exoskeletons where the dynamics of the entire exoskeleton should be compensated.

Reinforcement Learning Control of Robotic Knee with Human in the Loop by Flexible Policy Iteration

This study is motivated by a new class of challenging control problems described by automatic tuning of robotic knee control parameters with human in the loop. In addition to inter-person and intra-person variances inherent in such human-robot systems, human user safety and stability, as well as data and time efficiency should also be taken into design consideration. Here by data and time efficiency we mean learning and adaptation of device configurations takes place within countable gait cycles or within minutes of time. As solutions to this problem is not readily available, we therefore propose a new policy iteration based adaptive dynamic programming algorithm, namely the flexible policy iteration (FPI). We show that the FPI solves the control parameters via (weighted) least-squares while it incorporates data flexibly and utilizes prior knowledge. We provide analyses on stable control policies, non-increasing and converging value functions to Bellman optimality, and error bounds on the iterative value functions subject to approximation errors. We extensively evaluated the performance of FPI in a well-established locomotion simulator, the OpenSim under realistic conditions. By inspecting FPI with three other comparable algorithms, we demonstrate the FPI as a feasible data and time efficient design approach for adapting the control parameters of the prosthetic knee to co-adapt with the human user who also places control on the prosthesis. As the proposed FPI algorithm does not require stringent constraints or peculiar assumptions, we expect this reinforcement learning controller can potentially be applied to other challenging adaptive optimal control problems.