In this paper we combine Gaussian process regression and impedance control, to illicit robust, anthropomorphic, adaptive control of a powered ankle prosthesis. We learn the non-linear manifolds which guide how locomotion variables temporally evolve, and regress that surface over a velocity range to create a manifold. The joint set of manifolds, as well as the temporal evolution of the gait-cycle duration is what we term a locomotion envelope. Current powered prostheses have problems adapting across speeds. It is likely that humans rely upon a control strategy which is adaptable, can become more robust and accurate with more data and provides a nonparametric approach which allows the strategy to grow with the number of observations. We demonstrate such a strategy in this study and successfully simulate locomotion well beyond our training data. The method we propose is based on common physical features observed in numerous human subjects walking at different speeds. Based on the derived locomotion envelopes we show that ankle power increases monotonically with speed among all subjects. We demonstrate our methods in simulation and human experiments, on a powered ankle foot prosthesis to demonstrate the effectiveness of the method.

This paper focuses on the development of a dynamic model-free whole-body controller for a humanoid robot with high kinematic redundancy. The proposed controller is based on force-level operational-space control framework, which computes joint torques for the required forces of prioritized multiple tasks. While typical approaches based on this framework require to obtain an accurate robot dynamics model, which has been generally recognized as a major hurdle to overcome for implementation in real humanoid robots, the proposed controller incorporates adaptive sliding-mode and online dynamics estimation schemes; thus, it can be easily realized on a humanoid without identifying complex robot dynamic parameters. As a result, the gains of the proposed controller are adaptively adjusted to assure the control accuracy, when the humanoid robot changes its posture and undergoes uncertain disturbances. Experiments with a 23-DoFs humanoid under uncertain disturbances verify that the proposed controller can robustly perform multiple tasks with high accuracy.

A common disadvantage of multi-legged robots is that they often lack the manipulation capability. To overcome this limitation, an arm can be added to the body of the multi-legged robot, to perform manipulation tasks and provide assistance for locomotion. First, we proposed an attachment configuration of the arm for a multi-legged robot that provide a uniform workspace in front, below and above the base robot trunk. Second, an integrated control framework promises to keep the mobility and the balance of the mobile platform and provides precise manipulation capability of the arm incorporating a payload estimation scheme. Finally, we verify an integrated control framework with experimental results of a static and walking mobile platform while moving the arm.

This paper presents a passivity based compliance stabilizer for humanoid robots. The proposed stabilizer is an admittance controller that uses the force/torque sensing in feet to actively regulate the compliance for the position controlled system. The low stiffness provided by the stabilizer permits compliant interaction with external forces, and the active damping control guarantees the passivity by dissipating the excessive energy delivered by disturbances. Both the theoretical work and simulation validations are presented. The effectiveness of the stabilizer is demonstrated by the simulations of a simplified cart-table model and the multi-body model of a humanoid under impulsive/periodic force perturbations during standing and walking in place. Simulation data show the quantitative evaluation of the stabilization effect by comparing the responses of body attitude, center of mass, center of pressure without and with the stabilizer.

Keeping balance is the main concern for humanoids in standing and walking tasks. This paper endeavors to acquire optimal ankle stabilization methods for humanoids with passive and active compliance and explain ankle balancing strategy from the compliance regulation perspective. Unlike classical stiff humanoids, the compliant ones can control both impedance and position during task operation. Optimal compliance regulation is resolved to maximize the stability of the humanoids. The linearized model is proposed to obtain the optimal ankle impedance for stabilizing against impacts. The nonlinear model is proposed as well and compared with the linear one. The proposed methods are validated by experiments on an intrinsically compliant humanoid using passivity based admittance and impedance controllers both in joint and Cartesian space.

This paper presents the design of a novel compliant joint for high performance mobility. The design principle of the joint is based on an asymmetric compliant antagonistic scheme which is actuated by two motors of different power capability and efficiency. Torques from the two motors are transmitted to the joint through two elastic elements of different stiffness and energy storage capacity. The proposed compliant joint design combines high power performance, large energy storage capacity and physical resilience all necessary features for performing high performance mobility such as agile locomotion. The paper introduces the principle of operation, the design and mechanical implementation of the joint. Preliminary experimental trials demonstrate the joint performance in a single degree of freedom leg prototype system.