In robotics, structural design and behavior optimization have long been considered separate processes, resulting in the development of systems with limited capabilities. Recently, co-design methods have gained popularity, where bi-level formulations are used to simultaneously optimize the robot design and behavior for specific tasks. However, most implementations assume a serial or tree-type model of the robot, overlooking the fact that many robot platforms incorporate parallel mechanisms. In this paper, we present a first co-design formulation that explicitly incorporates parallel coupling constraints into the dynamic model of the robot. In this framework, an outer optimization loop focuses on the design parameters, in our case the transmission ratios of a parallel belt-driven manipulator, which map the desired torques from the joint space to the actuation space. An inner loop performs trajectory optimization in the actuation space, thus exploiting the entire dynamic range of the manipulator. We compare the proposed method with a conventional co-design approach based on a simplified tree-type model. By taking advantage of the actuation space representation, our approach leads to a significant increase in dynamic payload capacity compared to the conventional co-design implementation.

Trajectory optimization methods have become ubiquitous for the motion planning and control of underactuated robots for e.g., quadrupeds, humanoids etc. While they have been extensively used in the case of serial or tree type robots, they are seldomly used for planning and control of robots with closed loops. Series-parallel hybrid topology is quite commonly used in the design of humanoid robots, but they are often neglected during trajectory optimization and the movements are computed for a serial abstraction of the system and then the solution is mapped to the actuator coordinates. As a consequence, the full capability of the robot cannot be exploited. This paper presents a case study of trajectory optimization for series-parallel hybrid robot by taking into account all the holonomic constraints imposed by the closed kinematic loops present in the system. We demonstrate the advantages of this consideration with a weightlifting task on RH5 Manus humanoid in both simulation and experiments.

It is well established that a stiff structure along with an optimal mass distribution are key features to perform dynamic movements, and parallel designs provide these characteristics to a robot. This work presents the new upper-body design of the humanoid robot RH5 named RH5 Manus with series-parallel hybrid design. The new design choices allow us to perform dynamic motions including tasks that involve a payload of 4 kg in each hand and fast boxing motions. The parallel kinematics combined with an overall serial chain of the robot provides us with high force production along with a larger range of motion and low peripheral inertia. The robot is equipped with backdrivable actuators with current sensing, force-torque sensors, stereo camera, laser scanners, high-resolution encoders etc that provide interaction with operators and environment. We generate several diverse dynamic motions using trajectory optimization, and successfully execute them on the robot with accurate trajectory and velocity tracking, while respecting joint rotation, velocity, and torque limits.

Musical dancing is an ubiquitous phenomenon in the human society. Providing robots the ability to dance has the potential to make the human robot co-existence more acceptable in our society. Hence, dancing robots have generated a considerable research interest in the recent years. In this paper, we present a novel formalization of robot dancing as planning and control of optimally timed actions based on beat timings and additional features extracted from the music. We showcase the use of this formulation in three different variations: with input of human expert choreography, imitation of a predefined choreography, and automated generation of a novel choreography. Our method has been validated on four different musical pieces, both in simulation and on a real robot, using the upper-body humanoid robot RH5 Manus.