Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot

Reinhart F, Steil JJ. Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot. In: Procedia Technology. Vol 26. 2016: 12-19

Software Abstractions for Simulation and Control of a Continuum Robot

Nordmann A, Rolf M, Wrede S. Software Abstractions for Simulation and Control of a Continuum Robot. In: SIMPAR2012 - SIMULATION, MODELING, and PROGRAMMING for AUTONOMOUS ROBOTS. 2012

An Active Compliant Control Mode for Interaction with a Pneumatic Soft Robot

Queißer J, Neumann K, Rolf M, Reinhart F, Steil JJ. An Active Compliant Control Mode for Interaction with a Pneumatic Soft Robot. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE; 2014: 573-579.Bionic soft robots offer exciting perspectives for more flexible and safe physical interaction with the world and humans. Unfortunately, their hardware design often prevents analytical modeling, which in turn is a prerequisite to apply classical automatic control approaches. On the other hand, also modeling by means of learning is hardly feasible due to many degrees of freedom, high-dimensional state spaces and the softness properties like e.g. mechanical elasticity, which cause limited repeatability and complex dynamics. Nevertheless, the realization of basic control modes is important to leverage the potential of soft robots for applications. We therefore propose a hybrid approach combining classical and learning elements for the realization of an interactive control mode for an elastic bionic robot. It superimposes a low-gain feedback control with a feed-forward control based on a learned simplified model of the inverse dynamics which considers only equilibria of the robot’s dynamics. We demonstrate on the Bionic Handling Assistant how a respective inverse equilibrium model can be learned and effectively exploited for quick and agile control. In a second step, the control scheme is extended to an active compliant control mode. It implements a kind of gravitation compensation to allow for kinesthetic teaching of the robot based on the implicit knowledge of gravitational and mechanical forces that are encoded in the learned equilibrium model.We finally discuss that this control scheme may be implemented also on other soft robots to provide the avenue towards their applications in general manipulation tasks

A Multi-Level Control Architecture for the Bionic Handling Assistant

Rolf M, Neumann K, Queißer J, Reinhart F, Nordmann A, Steil JJ. A Multi-Level Control Architecture for the Bionic Handling Assistant. Advanced Robotics. 2015;29(13: SI):847-859.The Bionic Handling Assistant is one of the largest soft continuum robots and very special in be- ing a pneumatically operated platform that is able to bend, stretch, and grasp in all directions. It nevertheless shares many challenges with smaller continuum and other softs robots such as parallel actuation, complex movement dynamics, slow pneumatic actuation, non-stationary behavior, and a lack of analytic models. To master the control of this challenging robot, we argue for a tight inte- gration of standard analytic tools, simulation, control, and state of the art machine learning into an overall architecture that can serve as blueprint for control design also beyond the BHA. To this aim, we show how to integrate specific modes of operation and different levels of control in a synergistic manner, which is enabled by using modern paradigms of software architecture and middleware. We thereby achieve an architecture with unique overall control abilities for a soft continuum robot that allow for exible experimentation towards compliant user-interaction, grasping, and online learning of internal models

Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning

This paper presents an efficient learning-based method to solve the inverse kinematic (IK) problem on soft robots with highly non-linear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation for either forward or inverse kinematics. To address this challenge, we employ neural networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We first generate a large number of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. Thereafter, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware. The effectiveness of our approach has been verified on pneumatic-driven soft robots for path following and interactive positioning

Local Online Motor Babbling: Learning Motor Abundance of a Musculoskeletal Robot Arm

Motor babbling and goal babbling has been used for sensorimotor learning of highly redundant systems in soft robotics. Recent works in goal babbling have demonstrated successful learning of inverse kinematics (IK) on such systems, and suggest that babbling in the goal space better resolves motor redundancy by learning as few yet efficient sensorimotor mappings as possible. However, for musculoskeletal robot systems, motor redundancy can provide useful information to explain muscle activation patterns, thus the term motor abundance. In this work, we introduce some simple heuristics to empirically define the unknown goal space, and learn the IK of a 10 DoF musculoskeletal robot arm using directed goal babbling. We then further propose local online motor babbling guided by Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which bootstraps on the goal babbling samples for initialization, such that motor abundance can be queried online for any static goal. Our approach leverages the resolving of redundancies and the efficient guided exploration of motor abundance in two stages of learning, allowing both kinematic accuracy and motor variability at the queried goal. The result shows that local online motor babbling guided by CMA-ES can efficiently explore motor abundance at queried goal positions on a musculoskeletal robot system and gives useful insights in terms of muscle stiffness and synergy.IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS2019), November 4 - 8, 2019, Macau, Chin

Design, implementation and control of a deformable manipulator robot based on a compliant spine

International audienceThis paper presents the conception, the numerical modeling and the control of a dexterous, deformable manipulator bio-inspired by the skeletal spine found in vertebrate animals. Through the implementation of this new manipulator, we show a methodology based on numerical models and simulations, that goes from design to control of continuum and soft robots. The manipulator is modeled using Finite Element Method (FEM), using a set of beam elements that reproduce the lattice structure of the robot. The model is computed and inverted in real-time using optimisation methods. A closed-loop control strategy is implemented to account for the disparities between the model and the robot. This control strategy allows for accurate positioning, not only of the tip of the manipulator, but also the positioning of selected middle points along its backbone. In a scenario where the robot is piloted by a human operator, the command of the robot is enhanced by a haptic loop that renders the boundaries of its task space as well as the contact with its environment. The experimental validation of the model and control strategies is also presented in the form of an inspection task use case