Visualizing Quaternion Multiplication

Quaternion rotation is a powerful tool for rotating vectors in 3-D; as a result, it has been used in various engineering fields, such as navigations, robotics, and computer graphics. However, understanding it geometrically remains challenging, because it requires visualizing 4-D spaces, which makes exploiting its physical meaning intractable. In this paper, we provide a new geometric interpretation of quaternion multiplication using a movable 3-D space model, which is useful for describing quaternion algebra in a visual way. By interpreting the axis for the scalar part of quaternion as a 1-D translation axis of 3-D vector space, we visualize quaternion multiplication and describe it as a combined effect of translation, scaling, and rotation of a 3-D vector space. We then present how quaternion rotation formulas and the derivative of quaternions can be formulated and described under the proposed approach.112sciescopu

Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World

Experimentation on real robots is demanding in terms of time and costs. For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and benchmark algorithms. However, insights gained in simulation do not necessarily translate to real robots, in particular for tasks involving complex interactions with the environment. The Real Robot Challenge 2022 therefore served as a bridge between the RL and robotics communities by allowing participants to experiment remotely with a real robot - as easily as in simulation. In the last years, offline reinforcement learning has matured into a promising paradigm for learning from pre-collected datasets, alleviating the reliance on expensive online interactions. We therefore asked the participants to learn two dexterous manipulation tasks involving pushing, grasping, and in-hand orientation from provided real-robot datasets. An extensive software documentation and an initial stage based on a simulation of the real set-up made the competition particularly accessible. By giving each team plenty of access budget to evaluate their offline-learned policies on a cluster of seven identical real TriFinger platforms, we organized an exciting competition for machine learners and roboticists alike. In this work we state the rules of the competition, present the methods used by the winning teams and compare their results with a benchmark of state-of-the-art offline RL algorithms on the challenge datasets

Efficient Multitask Reinforcement Learning Without Performance Loss

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An Adaptive Model Uncertainty Estimator Using Delayed State Based Model-free Control and Its Application to Robot Manipulator

In this article, we propose an innovative model-free control (MFC) algorithm using an adaptive model uncertainty estimator (AMUE) that provides stable torque input while allowing more precise control, even in the presence of instantaneous disturbances, such as friction, payload, or trajectory changes. Unlike traditional time-delay estimation (TDE)-based controllers that directly use one-sample delayed signals to estimate unmodeled dynamics and uncertainties, the proposed algorithm achieves better tracking performance by considering not only the one-sample delayed signal but also its gradient with an adaptive gain. Furthermore, the proposed adaptive estimator works well independently of conventional TDE-based controllers, providing a wide range of control gains. This implies that the proposed approach provides the opportunity to strategically improve TDE-based controllers, which have performance limitations caused by conventional TDE technique errors. The proposed algorithm can be easily extended to different TDE-based controllers. Finally, the stability of the AMUE-MFC is guaranteed through the Lyapunov stability theory, and its performance is demonstrated via simulations and experiments with robotic manipulators. © 1996-2012 IEEE.11Nsciescopu

A Reinforcement Learning-based Adaptive Time-Delay Control and Its Application to Robot Manipulators

This study proposes an innovative reinforcement learning-based time-delay control (RL-TDC) scheme to provide more intelligent, timely, and aggressive control efforts than the existing simple-structured adaptive time-delay controls (ATDCs) that are well-known for achieving good tracking performances in practical applications. The proposed control scheme adopts a state-of-the-art RL algorithm called soft actor critic (SAC) with which the inertia gain matrix of the timedelay control is adjusted toward maximizing the expected return obtained from tracking errors over all the future time periods. By learning the dynamics of the robot manipulator with a data-driven approach, and capturing its intractable and complicated phenomena, the proposed RL-TDC is trained to effectively suppress the inherent time delay estimation (TDE) errors arising from time delay control, thereby ensuring the best tracking performance within the given control capacity limits. As expected, it is demonstrated through simulation with a robot manipulator that the proposed RL-TDC avoids conservative small control actions when large ones are required, for maximizing the tracking performance. It is observed that the stability condition is fully exploited to provide more effective control actions.1

Optimal tone curve characteristics of transparent display for preferred image reproduction

Preferred monitor gamma setting is compared between normal and 10%-transmittance transparent display by simulating indoor lighting condition using LCD monitor. Four test images are manipulated to have 10 different monitor gamma values both for normal and transparent display. Based on 10 observers' judgment, it is found that lower gamma setting is preferred for transparent display than that for normal display. The preferred gamma settings for normal and transparent display have the similar lightness difference between gray levels.open

Automatic Data Augmentation by Upper Confidence Bounds for Deep Reinforcement Learning

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Improved Robustness of Reinforcement Learning Based on Uncertainty and Disturbance Estimator

This paper proposes a method to improve the robustness of RLs based on model-free uncertainty and disturbance estimator (RL-based UDE). In the real environment, instead of using optimal trajectory and control techniques to perform complex tasks, it learns through RL and supplements robustness by using uncertainty and disturbance estimator (UDE). From UDE, the robotics system can be improved the stability by appropriately canceling the uncertainty and disturbance without efforts to obtain model information; hence the UDE can compensate for the performance degradation of RL when system is non-stationary. In addition, the performance can be improved by reducing the sensor noise from low-pass filter of UDE. It is shown through an experiment that the proposed RL-based UDE provides robustness.1

Parameter identification of lithium-ion battery pseudo-2-dimensional models using genetic algorithm and neural network cooperative optimization

The electrochemical model parameters of a lithium-ion battery are important indicators of its state-of-health, and many previous studies have proposed methods for identifying them. These identification methods must solve highly nonlinear optimization problems with many local optima. Hence, metaheuristic approaches are often employed. Most metaheuristics take a way to abandon worse solutions and make the most use of better solutions only. Such inefficient use of data leads to local optima problem in metaheuristics. To overcome these limitations, this paper proposes a novel parameter identification method in which a neural network cooperates with a genetic algorithm. The proposed method adopts an 1-dimensional convolutional neural network to learn the dynamics between the known input current and the corresponding simulated voltage. Although estimated parameters cause large output voltage errors, they are useful for building an electrochemical model and can be used to recommend highly probable parameter candidates. We clearly show through simulation and experiment that the electrochemical model parameters are identified more accurately and reliably compared with various existing results, owing to the high data efficiency of the proposed method.11Nscopu