WALK‐MAN: A High‐Performance Humanoid Platform for Realistic Environments

Abstract

In this work, we present WALK-MAN, a humanoid platform that has been developed to operate in realistic unstructured environment, and demonstrate new skills including powerful manipulation, robust balanced locomotion, high-strength capabilities, and physical sturdiness. To enable these capabilities, WALK-MAN design and actuation are based on the most recent advancements of series elastic actuator drives with unique performance features that differentiate the robot from previous state-of-the-art compliant actuated robots. Physical interaction performance is benefited by both active and passive adaptation, thanks to WALK-MAN actuation that combines customized high-performance modules with tuned torque/velocity curves and transmission elasticity for high-speed adaptation response and motion reactions to disturbances. WALK-MAN design also includes innovative design optimization features that consider the selection of kinematic structure and the placement of the actuators with the body structure to maximize the robot performance. Physical robustness is ensured with the integration of elastic transmission, proprioceptive sensing, and control. The WALK-MAN hardware was designed and built in 11 months, and the prototype of the robot was ready four months before DARPA Robotics Challenge (DRC) Finals. The motion generation of WALK-MAN is based on the unified motion-generation framework of whole-body locomotion and manipulation (termed loco-manipulation). WALK-MAN is able to execute simple loco-manipulation behaviors synthesized by combining different primitives defining the behavior of the center of gravity, the motion of the hands, legs, and head, the body attitude and posture, and the constrained body parts such as joint limits and contacts. The motion-generation framework including the specific motion modules and software architecture is discussed in detail. A rich perception system allows the robot to perceive and generate 3D representations of the environment as well as detect contacts and sense physical interaction force and moments. The operator station that pilots use to control the robot provides a rich pilot interface with different control modes and a number of teleoperated or semiautonomous command features. The capability of the robot and the performance of the individual motion control and perception modules were validated during the DRC in which the robot was able to demonstrate exceptional physical resilience and execute some of the tasks during the competition

Generalization of Series Elastic Actuator Configurations and Dynamic Behavior Comparison

“…According to this aspect, several studies have focused on a design of spring itself to increase the control bandwidth with high stiffness [29]. The proposed general configuration of SEA can be exploited also from this perspective; the gear ratio distribution between α and β in the proposed configuration of Figure 3 In order to exhibit the influence of the gear ratio distribution on the natural bandwidth of SEA, the open-loop frequency response plot is shown in Figure 22.…”

Visual end-effector tracking using a 3D model-aided particle filter for humanoid robot platforms

“…The proposed approach paves the way to a number of possible future work comprising: I ) joint estimation of the pose of the end-effector and of the object to manipulate using, e.g., the random Random Finite Set (RFS) filtering approach [49]- [54]; II ) use data fusion techniques to exploit multiple features to improve both performance and robustness of the recursive Bayesian filtering; III ) conduct tests on different robot platform like, e.g., WALK-MAN [2]; IV ) distribute a Free and Open Source Software (FOSS) implementation of the algorithms to the community. …”

“…Humanoid robots are designed to work in an unstructured and possibly complex environment where humans live [1], [2] and they rely on a wide variety of sensors to perceive and interact with it. The perception and interaction capabilities of a humanoid robot with such complexities depend on the available sensors.…”

Optimal Control of a Differentially Flat Two-Dimensional Spring-Loaded Inverted Pendulum Model

“…Apart from being important on its own, solution to the stance phase problem for all possible stance states constructs a value function for the stance dynamics. Such a value function serves as an important piece for solving the overall optimal control problem (9). Henceforth, we denote by V S the value function for the stance phase problem.…”