From RGB images to Dynamic Movement Primitives for planar tasks

DMP have been extensively applied in various robotic tasks thanks to their generalization and robustness properties. However, the successful execution of a given task may necessitate the use of different motion patterns that take into account not only the initial and target position but also features relating to the overall structure and layout of the scene. To make DMP applicable to a wider range of tasks and further automate their use, we design a framework combining deep residual networks with DMP, that can encapsulate different motion patterns of a planar task, provided through human demonstrations on the RGB image plane. We can then automatically infer from new raw RGB visual input the appropriate DMP parameters, i.e. the weights that determine the motion pattern and the initial/target positions. We compare our method against another SoA method for inferring DMP from images and carry out experimental validations in two different planar tasks

Efficient DMP generalization to time-varying targets, external signals and via-points

Dynamic Movement Primitives (DMP) have found remarkable applicability and success in various robotic tasks, which can be mainly attributed to their generalization and robustness properties. Nevertheless, their generalization is based only on the trajectory endpoints (initial and target position). Moreover, the spatial generalization of DMP is known to suffer from shortcomings like over-scaling and mirroring of the motion. In this work we propose a novel generalization scheme, based on optimizing online the DMP weights so that the acceleration profile and hence the underlying training trajectory pattern is preserved. This approach remedies the shortcomings of the classical DMP scaling and additionally allows the DMP to generalize also to intermediate points (via-points) and external signals (coupling terms), while preserving the training trajectory pattern. Extensive comparative simulations with the classical and other DMP variants are conducted, while experimental results validate the applicability and efficacy of the proposed method

A Robust Controller for Stable 3D Pinching using Tactile Sensing

This paper proposes a controller for stable grasping of unknown-shaped objects by two robotic fingers with tactile fingertips. The grasp is stabilised by rolling the fingertips on the contact surface and applying a desired grasping force to reach an equilibrium state. The validation is both in simulation and on a fully-actuated robot hand (the Shadow Modular Grasper) fitted with custom-built optical tactile sensors (based on the BRL TacTip). The controller requires the orientations of the contact surfaces, which are estimated by regressing a deep convolutional neural network over the tactile images. Overall, the grasp system is demonstrated to achieve stable equilibrium poses on various objects ranging in shape and softness, with the system being robust to perturbations and measurement errors. This approach also has promise to extend beyond grasping to stable in-hand object manipulation with multiple fingers.Comment: 8 pages, 10 figures, 1 appendix. Accepted for publication in IEEE Robotics and Automation Letters and in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021). Supplemental video: https://youtu.be/rfQesw3FDA

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We consider the problem of impedance control for the physical interaction between the soft tip of a robot finger, where the nonlinear characteristics of the reproducing force and the finger dynamic parameters are unknown, and a rigid object or environment under kinematic uncertainties arising from both uncertain contact point location and uncertain rigid object geometry. An adaptive controller is proposed, and the asymptotic stability of the force regulation problem is shown for the planar case even when finger kinematics and rigid surface orientation are uncertain. Confirmation of the theoretical findings is done through simulation of a 3-degree-of-freedom planar robotic finger. 1

On the stability of T-S fuzzy control for non-linear systems

This work concerns the stability analysis of a non-linear system controlled by a fuzzy T-S control law. It is shown that the closed loop system is in general expressed by a T-S fuzzy system composed of rules with affine linear systems in their consequent parts. The stability of affine T-S systems is then investigated for a special case using as an example the regulation problem of a single link robot arm. Stability conditions are derived using the indirect and direct Lyapunov method and simulation results are presented

Operational space robot control for motion performance and safe interaction under Unintentional Contacts

A control law achieving motion performance of quality and compliant reaction to unintended contacts for robot manipulators is proposed in this work. It achieves prescribed performance evolution of the position error under disturbance forces up to a tunable level of magnitude. Beyond this level, it deviates from the desired trajectory complying to what is now interpreted as unintentional contact force, thus achieving enhanced safety by decreasing interaction forces. The controller is a passivity model based controller utilizing an artificial potential that induces vanishing vector fields. Simulation results with a three degrees of freedom (DOF) robot under the control of the proposed scheme, verify theoretical findings and illustrate motion performance and compliance under an external force of short duration in comparison with a switched impedance scheme

A Model-Free Controller for Guaranteed Prescribed Performance Tracking of Both Robot Joint Positions and Velocities

The problem of robot joint position and velocity tracking with prescribed performance guarantees is considered. The proposed controller is able to guarantee a prescribed transient and steady state behavior for the position and the velocity tracking errors without utilizing either the robot dynamic model or any approximation structures. Its performance is demonstrated and assessed via experiments with a KUKA LWR4+ arm