Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human Elbows

This study presents a pioneering effort to replicate human neuromechanical experiments within a virtual environment utilising a digital human model. By employing MyoSuite, a state-of-the-art human motion simulation platform enhanced by Reinforcement Learning (RL), multiple types of impedance identification experiments of human elbow were replicated on a musculoskeletal model. We compared the elbow movement controlled by an RL agent with the motion of an actual human elbow in terms of the impedance identified in torque-perturbation experiments. The findings reveal that the RL agent exhibits higher elbow impedance to stabilise the target elbow motion under perturbation than a human does, likely due to its shorter reaction time and superior sensory capabilities. This study serves as a preliminary exploration into the potential of virtual environment simulations for neuromechanical research, offering an initial yet promising alternative to conventional experimental approaches. An RL-controlled digital twin with complete musculoskeletal models of the human body is expected to be useful in designing experiments and validating rehabilitation theory before experiments on real human subjects.

Fractal Impedance for Passive Controllers

During the last decade, there has been increasing interest in new control frameworks to move robots from their industrial cages to unstructured environments where they may coexist with humans. Despite significant improvement in some specific applications (e.g., medical robotics), there is still the need of a general control framework to improve the robots' dynamics interaction performance without limiting system safety. The passive control framework has shown promising results in this direction; however, it relies on virtual energy tanks that can guarantee passivity as long as they do not run out of energy. In this paper, a fractal attractor is proposed to implement a variable impedance controller that is able to retain passivity without relying on the energy tank framework. The results prove that the proposed method can accurately track trajectories and apply forces at the end-effector. Furthermore, it can automatically deal with the extra energy introduced by changes in interaction conditions, null-space controller and environment. Therefore, These properties make the controller ideal for applications where the dynamic interaction at the end-effector is difficult to be characterized in advance, such as human-robot interaction and unknown dynamics.

Mechanical design of a distal scanner for confocal microlaparoscope: A conic solution

This paper presents the mechanical design of a distal scanner to perform a spiral scan for mosaic-imaging with a confocal microlaparoscope. First, it is demonstrated with ex vivo experiments that a spiral scan performs better than a raster scan on soft tissue. Then a mechanical design is developed in order to perform the spiral scan. The design in this paper is based on a conic structure with a particular curved surface. The mechanism is simple to implement and to drive; therefore, it is a low-cost solution. A 5:1 scale prototype is implemented by rapid prototyping and the requirements are validated by experiments. The experiments include manual and motor drive of the system. The manual drive demonstrates the resulting spiral motion by drawing the tip trajectory with an attached pencil. The motor drive demonstrates the speed control of the system with an analysis of video thread capturing the trajectory of a laser beam emitted from the tip.