We present an avatar system designed to facilitate the embodiment of humanoid robots by human operators, validated through iCub3, a humanoid developed at the Istituto Italiano di Tecnologia. More precisely, the paper makes two contributions: First, we present the humanoid iCub3 as a robotic avatar that integrates the latest significant improvements after about 15 years of development of the iCub series. Second, we present a versatile avatar system enabling humans to embody humanoid robots encompassing aspects such as locomotion, manipulation, voice, and facial expressions with comprehensive sensory feedback including visual, auditory, haptic, weight, and touch modalities. We validated the system by implementing several avatar architecture instances, each tailored to specific requirements. First, we evaluated the optimized architecture for verbal, nonverbal, and physical interactions with a remote recipient. This testing involved the operator in Genoa and the avatar in the Biennale di Venezia, Venice—about 290 kilometers away—thus allowing the operator to visit the Italian art exhibition remotely. Second, we evaluated the optimized architecture for recipient physical collaboration and public engagement on stage, live, at the We Make Future show, a prominent world digital innovation festival. In this instance, the operator was situated in Genoa while the avatar operated in Rimini—about 300 kilometers away—interacting with a recipient who entrusted the avatar with a payload to carry on stage before an audience of approximately 2000 spectators. Third, we present the architecture implemented by the iCub Team for the All Nippon Airways (ANA) Avatar XPrize competition.

In this work, a generalized low-level controller is presented for sensor collection, motor input, and networking with a high-level controller. In hierarchically controlled exoskeletal systems, which utilize series elastic actuators (SEAs), the hardware for sensor collection and motor command is separated from the computationally expensive high-level controller algorithm. The low-level controller is a hardware device that must collect sensor feedback, condition and filter the measurements, send actuator inputs, and network with the high-level controller at a real-time rate. This research outlines the hardware of two printed circuit board (PCB) designs for collecting and conditioning sensor feedback from two SEA subsystems and an inertial measurement unit (IMU). The SEAs have a joint and motor encoder, motor current, and force sensor feedback that can be measured using the proposed generalized low-level controller presented in this work. In addition, the high and low-level networking approach is discussed in detail, with a full breakdown of the data storage within a communication frame during the run-time operation. The challenges of device synchronization and updates rates of high and low-level controllers are also discussed. Further, the low-level controller was validated using a pendulum test bed, complete with full sensor feedback, including IMU results for two open-loop scenarios. Moreover, this work can be extended to other hierarchically controlled robotic systems that utilize SEA subsystems, such as humanoid robots, assistive rehabilitation robots, training simulators, and robotic-assisted surgical devices. The hardware and software designs presented in this work are available open source to enable researchers with a direct solution for data acquisition and the control of low-level devices in a robotic system.

In this work, the joint-control strategy is presented for the humanoid robot, PANDORA, whose structural components are designed to be compliant. As opposed to contemporary approaches which design the elasticity internal to the actuator housing, PANDORA's structural components are designed to be compliant under load or, in other words, structurally elastic. To maintain the rapid design benefit of additive manufacturing, this joint control strategy employs a disturbance observer (DOB) modeled from an ideal elastic actuator. This robust controller treats the model variation from the structurally elastic components as a disturbance and eliminates the need for system identification of the 3D printed parts. This enables mechanical design engineers to iterate on the 3D printed linkages without requiring consistent tuning from the joint controller. Two sets of hardware results are presented for validating the controller. The first set of results are conducted on an ideal elastic actuator testbed that drives an unmodeled, 1 DoF weighted pendulum with a 10 kg mass. The results support the claim that the DOB can handle significant model variation. The second set of results is from a robust balancing experiment conducted on the 12 DoF lower body of PANDORA. The robot maintains balance while an operator applies 50 N pushes to the pelvis, where the actuator tracking results are presented for the left leg.

In this work, the novel, open-source humanoid robot, PANDORA, is presented where a majority of the structural elements are manufactured using 3D-printed compliant materials. As opposed to contemporary approaches that incorporate the elastic element into the actuator mechanisms, PANDORA is designed to be compliant under load, or in other words, structurally elastic. This design approach lowers manufacturing cost and time, design complexity, and assembly time while introducing controls challenges in state estimation, joint and whole-body control. This work features an in-depth description on the mechanical and electrical subsystems including details regarding additive manufacturing benefits and drawbacks, usage and placement of sensors, and networking between devices. In addition, the design of structural elastic components and their effects on overall performance from an estimation and control perspective are discussed. Finally, results are presented which demonstrate the robot completing a robust balancing objective in the presence of disturbances and stepping behaviors.