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This paper might provide some answers https://arxiv.org/pdf/1810.05762.pdf For the observations / states they used not only angles, but also velocities, heights and positions (Table 2). In 4.2 Learning algorithm you can see that they mention this, which is related to your question about normalization: Additionally, for stability we whiten the current ...

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If I had to implement a path exploration/finding algorithm on a robot, I would follow these steps: Make sure you can detect your position. You need to be able to record your position otherwise you have no reference for the exploration. You don't need a global positioning system (like GPS), a local one is more than enough in your case. This means that the ...

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The abbreviations sim2sim, sim2real and real2real refer to techniques that can be used to transfer knowledge from one environment (e.g. in simulation) to another one (e.g. in the real world). sim2sim stands for simulation-to-simulation, sim2real stands for simulation-to-real, and real2real stands for real-to-real. In sim2sim, knowledge acquired during ...

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There is some research on this topic. See, for example, the papers Robot Identification and Localization with Pointing Gestures (2018) and Proximity Human-Robot Interaction Using Pointing Gestures and a Wrist-mounted IMU (2019), by Boris Gromov et al., where the human is assumed to possess an inertial measurement unit (IMU) attached to the arm

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Simply we say AI is software and robot is its body. This is because the algorithms we commonly think of AI come in the form of software, where when we talk about robots, we're talking about physical automation. In an automobile manufacturing process where automation is used, the software makes the decisions on what physical action the robot arm should ...

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Welcome to AI.SE @EdouardLopez! Because Boston Dynamics is a private, for profit, company, we cannot know for sure how they achieve their results. However, we can examine the available public information and make educated guesses. In the information posted with the video, Boston Dynamics tells us that they use ... an optimization algorithm transforms ...

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The rotation matrix $R_k(\theta)$ associated with a given unit-length vector $k$ and angle $\theta$ is given by the following formula \small{R_k(\theta) = \begin{bmatrix} \cos \theta +k_x^2 \left(1-\cos \theta\right) & k_x k_y \left(1-\cos \theta\right) - k_z \sin \theta & k_x k_z \left(1-\cos \theta\right) + k_y \sin \theta \\ k_y k_x \left(1-\...

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There is a YouTube video LEGO EV3 Raspberry Pi Tensorflow Sorting Machine by ebswift that should help you although you will need a Raspberry Pi. From the abstract: This is a sorting machine based on the EV3 45544 education kit sorting machine. The colour sorting camera is substituted with a Raspberry Pi with the v2 camera. The EV3 is controlled over wifi ...

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To be precise, a discrete Fourier transform can be used to transform a finite set of samples between frequency and time domains. A continuous Fourier transform can be applied in calculus to an expression or a set of equations (through the appropriate techniques) or used to develop algorithms, but digital systems are not continuous, so there is no way to ...

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The filtering (or convolution) process in Convolutional Neural Networks can be implemented using Fourier Transforms. Convolutions in image (or spatial) space are equivalent to multiplications in frequency space, and multiplications can be performed much more efficiently than convolutions. The algorithm for using Fourier Transforms to calculate a convolution ...

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It depends a lot on the hardware of your robot arm. Assuming that your servos have encoder information, if you have access to servos that have limited control like "rotate left/rotate right" functionality, you can phrase the your action space to be ["move left", "stop", "move right"]. In this way you can implement a discrete action space with 3 actions per ...

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