###### Abstract  
This paper proposes an adaptive human-robot concurrent control scheme that achieves the appropriate gait trajectory for a robotic leg prosthesis to improve the wearer's comfort in various tasks. To accommodate different wearers, a neuromuscular-force-based impedance method was developed using muscle activation to reshape gait trajectory. To eliminate the use of sensors for torque measurement, a disturbance observer was established to estimate the interaction force between the human residual limb and the prosthetic receptacle. The cost function was combined with the interaction force and tracking errors of the joints. We aim to reduce the cost function by minimally changing the control weight of the gait trajectory generated by the Central Pattern Generator (CPG). The control scheme was primarily based on human-in-the-loop optimization to search for a suitable control weight to regenerate the appropriate gait trajectory. To handle the uncertainties and unknown coupling of the motors, an adaptive law was designed to estimate the unknown parameters of the system. Through a stability analysis, the control framework was verified by semi-globally uniformly ultimately bounded stability. Experimental results are discussed, and the effectiveness of the adaptive control framework is demonstrated. In Case 1, the mean error (MEAN) of the tracking performance was 3.6\({}^{\circ}\) and 3.3\({}^{\circ}\), respectively. And the minimized mean square errors (MSEs) of the tracking performance were 2.3\({}^{\circ}\) and 2.8\({}^{\circ}\), respectively. In Case 2, the mean error (MEAN) of the tracking performance is 2.7\({}^{\circ}\) and 3.1\({}^{\circ}\), respectively. And the minimized mean square errors (MSEs) of the tracking performance are 1.8\({}^{\circ}\) and 2.4\({}^{\circ}\), respectively. In Case 3, the mean errors (MEANs) of the tracking performance for subject1 and 2 are 2.4\({}^{\circ}\), 2.9\({}^{\circ}\), 3.4\({}^{\circ}\), and 2.2\({}^{\circ}\), 2.8\({}^{\circ}\), 3.1\({}^{\circ}\), respectively. The minimized mean square errors (MSEs) of the tracking performance for subject1 and 2 were 1.6\({}^{\circ}\), 2.3\({}^{\circ}\), 2.6\({}^{\circ}\), and 1.3\({}^{\circ}\), 1.7\({}^{\circ}\), 2.2\({}^{\circ}\), respectively. robotic leg prosthesis; multi-task walking; concurrent adaptive control; human-in-the-loop optimization 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 2020-2020, 2  
receiver plays an important role in improving the satisfaction and comfort of amputees. Moreover, to accommodate different wearers, gait trajectory should be reshaped to increase the mobility of lower-limb amputees [4]. The requirement for a comfortable feeling for a wearer with a leg prosthesis is largely related to the physical interaction between the prosthesis and stump, as well as trajectory tracking [5]. While the leg prosthesis in practical applications is well defined and largely invariable, the demands are more diverse when assisting the subject, encompassing various tasks and different wearers. Therefore, the ability of online learning to perform various walking tasks is important for leg prostheses to provide effective assistance [6]. Hence, an adaptive framework based on human-in-the-loop (HIL) optimization was introduced to improve the online learning ability of leg prostheses [7]. It is important for the leg prosthesis to adaptively regulate the control parameters. In [8], the control method was modeled based on unified data-driven impedance control. The control parameters were obtained from a previously collected kinematic data set of eight normal humans. The impedance parameters can be regulated under different walking conditions. In [9], a control method based on a finite-state machine (FSM) was proposed and compared to the performance of direct myoelectric control. The control parameters can be varied for different walking tasks. In [10], the mechanical structure of a leg prosthesis was redesigned and developed using a high-torque actuator and low-reduction transmission.