Human Like Adaptation of Force and Impedance in Stable and Unstable Tasks

Abstract—This paper presents a novel human-like learning con-troller to interact with unknown environments. Strictly derived from the minimization of instability, motion error, and effort, the controller compensates for the disturbance in the environment in interaction tasks by adapting feedforward force and impedance. In contrast with conventional learning controllers, the new controller can deal with unstable situations that are typical of tool use and gradually acquire a desired stability margin. Simulations show that this controller is a good model of human motor adaptation. Robotic implementations further demonstrate its capabilities to optimally adapt interaction with dynamic environments and humans in joint torque controlled robots and variable impedance actuators, with-out requiring interaction force sensing. Index Terms—Feedforward force, human motor control, impedance, robotic control. I

A dataset of continuous affect annotations and physiological signals for emotion analysis

From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, direct, real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as it focusses on real-time continuous annotation of emotions, as experienced by the participants, while watching various videos. For this purpose, a novel, intuitive joystick-based annotation interface was developed, that allowed for simultaneous reporting of valence and arousal, that are instead often annotated independently. In parallel, eight high quality, synchronized physiological recordings (1000 Hz, 16-bit ADC) were made of ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature. The dataset consists of the physiological and annotation data from 30 participants, 15 male and 15 female, who watched several validated video-stimuli. The validity of the emotion induction, as exemplified by the annotation and physiological data, is also presented.Comment: Dataset available at: https://rmc.dlr.de/download/CASE_dataset/CASE_dataset.zi

Closed-Loop Behavior of an Autonomous Helicopter Equipped with a Robotic Arm for Aerial Manipulation Tasks

This paper is devoted to the control of aerial robots interacting physically with objects in the environment and with other aerial robots. The paper presents a controller for the particular case of a small‐scaled autonomous helicopter equipped with a robotic arm for aerial manipulation. Two types of influences are imposed on the helicopter from a manipulator: coherent and non ‐ coherent influence. In the former case, the forces and torques imposed on the helicopter by the manipulator change with frequencies close to those of the helicopter movement. The paper shows that even small interaction forces imposed on the fuselage periodically in proper phase could yield to low frequency instabilities and oscillations, so called phase circle

Force, impedance and trajectory learning for contact tooling and haptic identification

Humans can skilfully use tools and interact with the environment by adapting their movement trajectory, contact force, and impedance. Motivated by the human versatility, we develop here a robot controller that concurrently adapts feedforward force, impedance, and reference trajectory when interacting with an unknown environment. In particular, the robot's reference trajectory is adapted to limit the interaction force and maintain it at a desired level, while feedforward force and impedance adaptation compensates for the interaction with the environment. An analysis of the interaction dynamics using Lyapunov theory yields the conditions for convergence of the closed-loop interaction mediated by this controller. Simulations exhibit adaptive properties similar to human motor adaptation. The implementation of this controller for typical interaction tasks including drilling, cutting, and haptic exploration shows that this controller can outperform conventional controllers in contact tooling

Walk-through programming for robotic manipulators based on admittance control

The present paper addresses the issues that should be covered in order to develop walk-through programming techniques (i.e. a manual guidance of the robot) in an industrial scenario. First, an exact formulation of the dynamics of the tool the human should feel when interacting with the robot is presented. Then, the paper discusses a way to implement such dynamics on an industrial robot equipped with an open robot control system and a wrist force/torque sensor, as well as the safety issues related to the walk-through programming. In particular, two strategies that make use of admittance control to constrain the robot motion are presented. One slows down the robot when the velocity of the tool centre point exceeds a specified safety limit, the other one limits the robot workspace by way of virtual safety surfaces. Experimental results on a COMAU Smart Six robot are presented, showing the performance of the walk-through programming system endowed with the two proposed safety strategies

One-Dimensional Solution Families of Nonlinear Systems Characterized by Scalar Functions on Riemannian Manifolds

For the study of highly nonlinear, conservative dynamic systems, finding special periodic solutions which can be seen as generalization of the well-known normal modes of linear systems is very attractive. However, the study of low-dimensional invariant manifolds in the form of nonlinear normal modes is rather a niche topic, treated mainly in the context of structural mechanics for systems with Euclidean metrics, i.e., for point masses connected by nonlinear springs. Newest results emphasize, however, that a very rich structure of periodic and low-dimensional solutions exist also within nonlinear systems such as elastic multi-body systems encountered in the biomechanics of humans and animals or of humanoid and quadruped robots, which are characterized by a non-constant metric tensor. This paper discusses different generalizations of linear oscillation modes to nonlinear systems and proposes a definition of strict nonlinear normal modes, which matches most of the relevant properties of the linear modes. The main contributions are a theorem providing necessary and sufficient conditions for the existence of strict oscillation modes on systems endowed with a Riemannian metric and a potential field as well as a constructive example of designing such modes in the case of an elastic double pendulum

A versatile biomimetic controller for contact tooling and haptic exploration

International audienceThis article presents a versatile controller that enables various contact tooling tasks with minimal prior knowledge of the tooled surface. The controller is derived from results of neuroscience studies that investigated the neural mechanisms utilized by humans to control and learn complex interactions with the environment. We demonstrate here the versatility of this controller in simulations of cutting, drilling and surface exploration tasks, which would normally require different control paradigms. We also present results on the exploration of an unknown surface with a 7-DOF manipulator, where the robot builds a 3D surface map of the surface profile and texture while applying constant force during motion. Our controller provides a unified control framework encompassing behaviors expected from the different specialized control paradigms like position control, force control and impedance control

EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems

This paper proposes a Nonlinear Model-Predictive Control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation

EigenMPC:An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems

This paper proposes a Nonlinear Model-Predictive Control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation