#Webots simulation speaker software#
#Webots simulation speaker simulator#

We present SIERRA, a novel framework structured around that idea for accelerating research development and improving reproducibility of results. Modern intelligent systems researchers form hypotheses about system behavior and then run experiments using one or more independent variables to test their hypotheses. This demonstrates a new use of Qualitative Spatial Relations applied to the problem of intention reading in the domain of human-robot interaction. We have tested this architecture in a simulated kitchen environment and the results we have collected show that the robot is able to both recognize an ongoing goal and to properly collaborate towards its achievement.

This is performed through an ensemble of parallel processes that model a low-level action recognition and a high-level goal understanding, both of which are formally verified. In this paper, we present CASPER (Cognitive Architecture for Social Perception and Engagement in Robots): a symbolic cognitive architecture that uses qualitative spatial reasoning to anticipate the pursued goal of another agent and to calculate the best collaborative behavior. In other words, we want these robots to understand the intentions of their partners with the purpose of predicting the best way to help them. To seamlessly integrate themselves in our society, these machines should possess the ability to navigate the complexities of our daily routines even in the absence of a human's direct input. Our world is being increasingly pervaded by intelligent robots with varying degrees of autonomy. We demonstrate our method on robotic hardware (e.g., manipulator arm), and show that with the learned residual errors, we can further close the reality gap between kinematic models, simulations, and the real robot. Using this method, we model these residual errors with only small amounts of data - a necessity as we improve the simulator/kinematic model by learning directly from hardware operation. This is achieved using auto-tuning and neural networks, where the parameters of a neural network are updated using an auto-tuning method that applies equations from an Unscented Kalman Filter (UKF) formulation.

#Webots simulation speaker simulator#

Thus, the objective of this work is to learn the residual errors between a kinematic and/or simulator model and the real robot. It is now the main commercial product available from Cyberbotics Ltd.Īchieving highly accurate kinematic or simulator models that are close to the real robot can facilitate model-based controls (e.g., model predictive control or linear-quadradic regulators), model-based trajectory planning (e.g., trajectory optimization), and decrease the amount of learning time necessary for reinforcement learning methods. WebotsTM has been developed in collaboration with the Swiss Federal Institute of Technology in Lausanne, thoroughly tested, well documented and continuously maintained for over 7 years. You can program these robots using your favorite development environment, simulate them and optionally transfer the resulting programs onto your real robots. You can equip each robot with a large number of available sensors and actuators. For each object, you can define a number of properties, such as shape, color, texture, mass, friction, etc. WebotsTM lets you define and modify a complete mobile robotics setup, even several different robots sharing the same environment. The provided robot libraries enable you to transfer your control programs to several commercially available real mobile robots.

#Webots simulation speaker software#

develops WebotsTM, a mobile robotics simulation software that provides you with a rapid prototyping environment for modelling, programming and simulating mobile robots.