Robot control

Robotic control is the system that contributes to the movement of robots. This involves the mechanical aspects and programmable systems that makes it possible to control robots. Robotics can be controlled by various means including manual, wireless, semi-autonomous (a mix of fully automatic and wireless control), and fully autonomous (using artificial intelligence). Modern robots (2000-present) Medical and surgical In the medical field, robots are used to make precise movements that are difficult for humans. Robotic surgery involves the use of less-invasive surgical methods, which are “procedures performed through tiny incisions”. Robots use the da Vinci surgical method, which involves the robotic arm (which holds onto surgical instruments) and a camera. The surgeon sits on a console where he controls the robot wirelessly. The feed from the camera is projected on a monitor, allowing the surgeon to see the incisions. The system is built to mimic the movement of

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Demo: Pointing Gestures for Proximity Interaction

We demonstrate a system to control robots in the users proximity with pointing gestures-a natural device that people use all the time to communicate with each other. Our setup consists of a miniature quadrotor Crazyflie 2.0, a wearable inertial measurement unit MetaWearR+ mounted on the user's wrist, and a laptop as the ground control station. The video of this demo is available at https://youtu.be/yafy-HZMk_U [1].

2019

Efficient Configuration Exploration in Inverse Dynamics Acquisition of Robotic Manipulators

Walid Amanhoud, Aude Billard, Sthithpragya Gupta, Farshad Khadivar

The inverse dynamics of a robotic manipulator is instrumental in precise robot control and manipulation. However, acquiring such a model is challenging, not only due to unmodelled non-linearities such as joint friction, but also from a machine learning perspective (e.g., input space dimension, amount of data needed). The accuracy of such models, regardless of the learning techniques, relies on proper excitation and exploration of the robot's configuration space, in order to collect a rich dataset. This study aims to provide rich data in learning the inverse dynamics of a serial robotic manipulator using supervised machine learning techniques. We propose a method, called Max-Information Configuration Exploration (MICE), to incrementally explore and generate information-rich data via computing parameters of a trajectory set. We also introduce a new set of excitation trajectories that explores robot's configuration through imposed stable limit cycles in robot joints' phase space while satisfying feasibility constraints and physical bounds. We benchmark MICE against state-of-the-art in terms of data quality and learning accuracy. The proposed methodology for data collection, model learning, and evaluation, is validated with a KUKA IIWA14 robotic arm where the results prove significant improvement over traditional approaches.

IEEE2021

Co-evolution of Morphology and Control of Virtual Legged Robots for the Steering Task

Auke Ijspeert, Ken Larpin, Soha Pouya, Jesse van den Kieboom

Co-evolution of morphology and control is a powerful approach in robotics to study performance on a particular task, considering mechanical structure and control as a whole to accomplish a certain goal. Co-evolutionary methods have been used to study the effect of these two fundamental components, in particular when applied to locomotion and reaching tasks. The resulting robots are usually evolved in simulation for a particular static task, making it difficult to transfer the results to a real robotic system, where a certain controllability (beyond the scope of the particular task) is desired. In this work, we are interested in evolving a robot with an externally controllable behavior, in particular considering locomotion tasks. We investigate to what extent co-evolution can outperform the more traditional approach of evolving only the robot control. The locomotion behavior is evolved for a simulated quadruped robot, using a multi-objective evolutionary algorithm, considering speed and energy efficiency as the main objectives. Additionally, we introduce a third objective where we provide the robot with an external command indicating the robot locomotion direction, allowing us to steer the robot once it is evolved. The resulting quadrupedal robots show improved performance on the steering task when compared to robots where only control is evolved.

2011

Artificial intelligence

Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of human beings or animals. AI applications include advanced web search engines (e.g., Google

MICRO-553: Haptic human robot interfaces

This course teaches basic knowledge on haptic devices, force feedback and mechanical man-machine interfaces. Lectures are about 40 %, the rest is hands-on practical work with the "haptic paddle", a complete mechanical device with full laptop control interface. Realization of project in groups of 2.