Trajectory Generation and Hardware

Related lectures (30)

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Explores trajectory generation in robot control, covering spatial aspects, interpolation methods, velocity profiles, and multi-connected points.

Explores error analysis and limitations in interpolation on evenly distributed nodes.

Covers the Newton method for finding zeros of functions using data interpolation.

Covers Lagrange interpolation using intervals to find accurate polynomial approximations.

Explores Bayes risk, generalization, error rates, and interpolation methods in machine learning.

Covers the optimization of error in polynomial interpolation, focusing on minimizing the error by strategically placing interpolation points.

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Introduces Lagrange interpolation for approximating data points with polynomials, discussing challenges and techniques for accurate interpolation.

Explores interpolation of regular functions, error analysis, convergence, and Chebyshev polynomials.

Explores trigonometric interpolation for approximating periodic functions and signals using equally spaced nodes.