Adaptive, fast walking in a biped robot under neuronal control and learning

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (> 3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks

Push recovery with stepping strategy based on time-projection control

In this paper, we present a simple control framework for on-line push recovery with dynamic stepping properties. Due to relatively heavy legs in our robot, we need to take swing dynamics into account and thus use a linear model called 3LP which is composed of three pendulums to simulate swing and torso dynamics. Based on 3LP equations, we formulate discrete LQR controllers and use a particular time-projection method to adjust the next footstep location on-line during the motion continuously. This adjustment, which is found based on both pelvis and swing foot tracking errors, naturally takes the swing dynamics into account. Suggested adjustments are added to the Cartesian 3LP gaits and converted to joint-space trajectories through inverse kinematics. Fixed and adaptive foot lift strategies also ensure enough ground clearance in perturbed walking conditions. The proposed structure is robust, yet uses very simple state estimation and basic position tracking. We rely on the physical series elastic actuators to absorb impacts while introducing simple laws to compensate their tracking bias. Extensive experiments demonstrate the functionality of different control blocks and prove the effectiveness of time-projection in extreme push recovery scenarios. We also show self-produced and emergent walking gaits when the robot is subject to continuous dragging forces. These gaits feature dynamic walking robustness due to relatively soft springs in the ankles and avoiding any Zero Moment Point (ZMP) control in our proposed architecture.Comment: 20 pages journal pape

Push Recovery of a Position-Controlled Humanoid Robot Based on Capture Point Feedback Control

In this paper, a combination of ankle and hip strategy is used for push recovery of a position-controlled humanoid robot. Ankle strategy and hip strategy are equivalent to Center of Pressure (CoP) and Centroidal Moment Pivot (CMP) regulation respectively. For controlling the CMP and CoP we need a torque-controlled robot, however most of the conventional humanoid robots are position controlled. In this regard, we present an efficient way for implementation of the hip and ankle strategies on a position controlled humanoid robot. We employ a feedback controller to compensate the capture point error. Using our scheme, a simple and practical push recovery controller is designed which can be implemented on the most of the conventional humanoid robots without the need for torque sensors. The effectiveness of the proposed approach is verified through push recovery experiments on SURENA-Mini humanoid robot under severe pushes

Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid

Hierarchical inverse dynamics based on cascades of quadratic programs have been proposed for the control of legged robots. They have important benefits but to the best of our knowledge have never been implemented on a torque controlled humanoid where model inaccuracies, sensor noise and real-time computation requirements can be problematic. Using a reformulation of existing algorithms, we propose a simplification of the problem that allows to achieve real-time control. Momentum-based control is integrated in the task hierarchy and a LQR design approach is used to compute the desired associated closed-loop behavior and improve performance. Extensive experiments on various balancing and tracking tasks show very robust performance in the face of unknown disturbances, even when the humanoid is standing on one foot. Our results demonstrate that hierarchical inverse dynamics together with momentum control can be efficiently used for feedback control under real robot conditions.Comment: 21 pages, 11 figures, 4 tables in Autonomous Robots (2015

A sensory-based adaptive walking control algorithm for variable speed biped robot gaits

A balance scheme for handling variable speed gaits was implemented on an experimental biped. The control scheme used pre-planned but adaptive motion sequences in combination with closed loop reactive control. CMAC neural networks were responsible for the adaptive control of side-to-side and front-to-back balance. The biped performance improved with neural network training. The biped was able to walk with variable speed gaits, and to change gait speeds on the fly. The slower gait speeds required statically balanced walking, while the faster speeds required dynamically balanced walking. It was not necessary to distinguish between the two balance modes within the controller. Following training, the biped was able to walk with continuous motion on flat, non-slippery surfaces at forward progression velocities in the range of 21 cm/min to 72 cm/min, with average stride lengths of 6.5 cm