Robots whose shape and stiffness are determined by internal forces generally have complex shape-stiffness relationships that depend on their structure. As a result, there are difficulties such as a decrease in shape reproducibility when the robot is not stiff, and a decrease in the range of motion when the robot is stiff. In this study, we propose a motion planning method that balances shape and stiffness by learning forward and inverse kinematics using a stochastic neural network (NN) and using the uncertainty that can be evaluated by the NN. Through experiments using a tensegrity manipulator with 40 actuators and 20 degrees of freedom in bending posture, we verify the validity of the proposed method.

In flexible redundantly-driven multi-DOF systems, like living beings, the representation of redundant kinematics including the diversity of solutions, is crucial for leveraging its distinctive characteristics. This paper proposes an active learning framework for forward and inverse modeling of complex kinematics that improves expressions of control space, task space, and null space. It consists of a Variational Auto Encoder (VAE)-type network that internally holds expressions of control space, task space, and null space, and an algorithm for selecting new data using the cross-entropy method. The validity of the proposed system was verified using a tensegrity manipulator driven by 40 pneumatic cylinders. As a result, it was confirmed that active learning contributed to achieving the entire range of motion covered and a well-organized representation of the null space.

This paper introduces a novel approach for modeling the dynamics of soft robots, utilizing a differentiable filter architecture. The proposed approach enables end-to-end training to learn system dynamics, noise characteristics, and temporal behavior of the robot. A novel spatio-temporal embedding process is discussed to handle observations with varying sensor placements and sampling frequencies. The efficacy of this approach is demonstrated on a tensegrity robot arm by learning end-effector dynamics from demonstrations with complex bending motions. The model is proven to be robust against missing modalities, diverse sensor placement, and varying sampling rates. Additionally, the proposed framework is shown to identify physical interactions with humans during motion. The utilization of a differentiable filter presents a novel solution to the difficulties of modeling soft robot dynamics. Our approach shows substantial improvement in accuracy compared to state-of-the-art filtering methods, with at least a 24% reduction in mean absolute error (MAE) observed. Furthermore, the predicted end-effector positions show an average MAE of 25.77mm from the ground truth, highlighting the advantage of our approach. The code is available at https://github.com/ir-lab/soft_robot_DEnKF.

This paper uses a data-driven approach to model a highly redundantly driven tensegrity manipulator's forward and inverse kinematics. The tensegrity manipulator is based on a class-1 tensegrity with 20 struts and bends by 40 pneumatic actuators whose internal pressures are independently controlled. Based on the data obtained through random trials with the robot, a VAE-based kinematics model is trained. The forward model, inverse model, and null space of kinematics are simultaneously acquired as subnetworks of the VAE-based kinematics model. Experiments confirmed that the subnetworks representing forward and inverse kinematics could be used for the end position estimation and control, respectively. In addition, the subnetwork representing null space can generate different target pressures that achieve the same end position, which was confirmed to mean variable stiffness properties similar to musculoskeletal robots.

This paper reports a tensegrity manipulator driven by 40 pneumatic cylinders without mechanical springs. In general, tensegrity robots use mechanical springs to achieve a stable/curved tensegrity structure, and this is true even when a component extends/retracts with an actuator. The stiffness of the mechanical spring should be high to increase the stiffness of the entire structure and improve the control response, but low to deform the structure. This fact means that the introduction of mechanical springs causes serious trade-offs in its design and control. In this study, we use pneumatic actuators not only for active deformation but also for passive. In this paper, we introduce the design and control system and then show the difference in response characteristics between the case with and without a spring, demonstrating the importance of the approach without a mechanical spring.