Online Bipedal Locomotion Adaptation for Stepping on Obstacles Using a Novel Foot Sensor

In this paper, we present a novel control architecture for the online adaptation of bipedal locomotion on inclined obstacles. In particular, we introduce a novel, cost-effective, and versatile foot sensor to detect the proximity of the robot's feet to the ground (bump sensor). By employing this sensor, feedback controllers are implemented to reduce the impact forces during the transition of the swing to stance phase or steeping on inclined unseen obstacles. Compared to conventional sensors based on contact reaction force, this sensor detects the distance to the ground or obstacles before the foot touches the obstacle and therefore provides predictive information to anticipate the obstacles. The controller of the proposed bump sensor interacts with another admittance controller to adjust leg length. The walking experiments show successful locomotion on the unseen inclined obstacle without reducing the locomotion speed with a slope angle of 12. Foot position error causes a hard impact with the ground as a consequence of accumulative error caused by links and connections' deflection (which is manufactured by university tools). The proposed framework drastically reduces the feet' impact with the ground.Comment: 6 pages, 2022 IEEE-RAS 21th International Conference on Humanoid Robots (Humanoids

Motion Planning and Control of Dynamic Humanoid Locomotion

Inspired by human, humanoid robots has the potential to become a general-purpose platform that lives along with human. Due to the technological advances in many field, such as actuation, sensing, control and intelligence, it finally enables humanoid robots to possess human comparable capabilities. However, humanoid locomotion is still a challenging research field. The large number of degree of freedom structure makes the system difficult to coordinate online. The presence of various contact constraints and the hybrid nature of locomotion tasks make the planning a harder problem to solve. Template model anchoring approach has been adopted to bridge the gap between simple model behavior and the whole-body motion of humanoid robot. Control policies are first developed for simple template models like Linear Inverted Pendulum Model (LIPM) or Spring Loaded Inverted Pendulum(SLIP), the result controlled behaviors are then been mapped to the whole-body motion of humanoid robot through optimization-based task-space control strategies. Whole-body humanoid control framework has been verified on various contact situations such as unknown uneven terrain, multi-contact scenarios and moving platform and shows its generality and versatility. For walking motion, existing Model Predictive Control approach based on LIPM has been extended to enable the robot to walk without any reference foot placement anchoring. It is kind of discrete version of \u201cwalking without thinking\u201d. As a result, the robot could achieve versatile locomotion modes such as automatic foot placement with single reference velocity command, reactive stepping under large external disturbances, guided walking with small constant external pushing forces, robust walking on unknown uneven terrain, reactive stepping in place when blocked by external barrier. As an extension of this proposed framework, also to increase the push recovery capability of the humanoid robot, two new configurations have been proposed to enable the robot to perform cross-step motions. For more dynamic hopping and running motion, SLIP model has been chosen as the template model. Different from traditional model-based analytical approach, a data-driven approach has been proposed to encode the dynamics of the this model. A deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics. The trained network is applied online to generate reference foot placements for the humanoid robot. Simulations have been performed to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions. The method proposed based on 2D SLIP model can be generalized to 3D SLIP model and the extension has been briefly mentioned at the end

Versatile Reactive Bipedal Locomotion Planning Through Hierarchical Optimization

© 2019 IEEE. When experiencing disturbances during locomotion, human beings use several strategies to maintain balance, e.g. changing posture, modulating step frequency and location. However, when it comes to the gait generation for humanoid robots, modifying step time or body posture in real time introduces nonlinearities in the walking dynamics, thus increases the complexity of the planning. In this paper, we propose a two-layer hierarchical optimization framework to address this issue and provide the humanoids with the abilities of step time and step location adjustment, Center of Mass (CoM) height variation and angular momentum adaptation. In the first layer, times and locations of consecutive two steps are modulated online based on the current CoM state using the Linear Inverted Pendulum Model. By introducing new optimization variables to substitute the hyperbolic functions of step time, the derivatives of the objective function and feasibility constraints are analytically derived, thus reduces the computational cost. Then, taking the generated horizontal CoM trajectory, step times and step locations as inputs, CoM height and angular momentum changes are optimized by the second layer nonlinear model predictive control. This whole procedure will be repeated until the termination condition is met. The improved recovery capability under external disturbances is validated in simulation studies

A Model Predictive Capture Point Control Framework for Robust Humanoid Balancing via Ankle, Hip, and Stepping Strategies

The robust balancing capability of humanoid robots against disturbances has been considered as one of the crucial requirements for their practical mobility in real-world environments. In particular, many studies have been devoted to the efficient implementation of the three balance strategies, inspired by human balance strategies involving ankle, hip, and stepping strategies, to endow humanoid robots with human-level balancing capability. In this paper, a robust balance control framework for humanoid robots is proposed. Firstly, a novel Model Predictive Control (MPC) framework is proposed for Capture Point (CP) tracking control, enabling the integration of ankle, hip, and stepping strategies within a single framework. Additionally, a variable weighting method is introduced that adjusts the weighting parameters of the Centroidal Angular Momentum (CAM) damping control over the time horizon of MPC to improve the balancing performance. Secondly, a hierarchical structure of the MPC and a stepping controller was proposed, allowing for the step time optimization. The robust balancing performance of the proposed method is validated through extensive simulations and real robot experiments. Furthermore, a superior balancing performance is demonstrated, particularly in the presence of disturbances, compared to a state-of-the-art Quadratic Programming (QP)-based CP controller that employs the ankle, hip, and stepping strategies. The supplementary video is available at https://youtu.be/CrD75UbYzdcComment: 19 pages,13 figure

Bipedal humanoid robot control by fuzzy adjustment of the reference walking plane

The two-legged humanoid structure has advantages for an assistive robot in the human living and working environment. A bipedal humanoid robot can avoid typical obstacles at homes and offices, reach consoles and appliances designed for human use and can be carried in human transport vehicles. Also, it is speculated that the absorption of robots in the human shape into the human society can be easier than that of other artificial forms. However, the control of bipedal walk is a challenge. Walking performance on solely even floor is not satisfactory. The complications of obtaining a balanced walk are dramatically more pronounced on uneven surfaces like inclined planes, which are quite commonly encountered in human surroundings. The difficulties lie in a variety of tasks ranging from sensor and data fusion to the design of adaptation systems which respond to changing surface conditions. This thesis presents a study on bipedal walk on inclined planes with changing slopes. A Zero Moment Point (ZMP) based gait synthesis technique is employed. The pitch angle reference for the foot sole plane −as expressed in a coordinate frame attached at the robot body − is adjusted online by a fuzzy logic system to adapt to different walking surface slopes. Average ankle pitch torques and the average value of the body pitch angle, computed over a history of a predetermined number of sampling instants, are used as the inputs to this system. The proposed control method is tested via walking experiments with the 29 degreesof- freedom (DOF) human-sized full-body humanoid robot SURALP (Sabanci University Robotics Research Laboratory Platform). Experiments are performed on even floor and inclined planes with different slopes. The results indicate that the approach presented is successful in enabling the robot to stably enter, ascend and leave inclined planes with 15 percent (8.5 degrees) grade. The thesis starts with a terminology section on bipedal walking and introduces a number of successful humanoid robot projects. A survey of control techniques for the walk on uneven surfaces is presented. The design and construction of the experimental robotic platform SURALP is discussed with the mechanical, electronic, walking reference generation and control aspects. The fuzzy reference adjustment system proposed for the walk on inclined planes is detailed and experimental results are presented

Sim-to-Real Learning of Robust Compliant Bipedal Locomotion on Torque Sensor-Less Gear-Driven Humanoid

In deep reinforcement learning, sim-to-real is the mainstream method as it needs a large number of trials, however, it is challenging to transfer trained policy due to reality gap. In particular, it is known that the characteristics of actuators in leg robots have a considerable influence on the reality gap, and this is also noticeable in high reduction ratio gears. Therefore, we propose a new simulation model of high reduction ratio gears to reduce the reality gap. The instability of the bipedal locomotion causes the sim-to-real transfer to fail catastrophically, making system identification of the physical parameters of the simulation difficult. Thus, we also propose a system identification method that utilizes the failure experience. The realistic simulations obtained by these improvements allow the robot to perform compliant bipedal locomotion by reinforcement learning. The effectiveness of the method is verified using a actual biped robot, ROBOTIS-OP3, and the sim-to-real transferred policy archived to stabilize the robot under severe disturbances and walk on uneven terrain without force and torque sensors.Comment: 8 pages. An accompanying video is available at the following link: https://youtu.be/fZWQq9yAYe