Learns the forcing term of a DMP from one demonstrated trajectory, so it can
later be regenerated (and generalised to a new goal or duration) by
dmpGenerate().
Usage
dmpFit(y, times = NULL, n_basis = 25L, alpha_z = 25)
Arguments
- y
Demonstration: a numeric vector (1-D) or T x D matrix (per-column
DOF).
- times
Optional time stamps (length T); defaults to 0..1.
- n_basis
Number of Gaussian basis functions (default 25).
- alpha_z
Attractor gain (default 25; damping beta_z = alpha_z / 4,
critically damped).
Value
a dmp object: learned weights (n_basis x D), goal, start,
duration, and parameters.
References
Ijspeert AJ, et al. (2013) Neural Comput 25:328-373.
Examples
t <- seq(0, 1, length.out = 100)
y <- 10 * (10*t^3 - 15*t^4 + 6*t^5) # min-jerk demo
d <- dmpFit(y); rg <- dmpGenerate(d)
max(abs(rg$y[, 1] - y)) # reproduces the demo
#> [1] 0.2561058