Notes of Gaussian Process for Dynamic Systems

Jonathan Ko, “Gaussian Process for Dynamic Systems”, PhD Thesis, University of Washington, 2011.

Bayes filter equation in Eq. 4.1 (p.34) has a typo (should be \propto, not ==)

p(xtz1:t,u1:t1)p(ztxt)p(xtxt1,ut1)p(xt1z1:t1,u1:t2)dxt1p(x_t|z_{1:t},u_{1:t-1}) \propto p(z_t|x_t) \int \textcolor{red}{p(x_t|x_{t-1},u_{t-1})} \textcolor{green}{p(x_{t-1}|z_{1:t-1},u_{1:t-2})} dx_{t-1}

  • Red\textcolor{red}{Red} part is dynamics model, describing how the state xx evolves in time based on the control input uu (p.34)
  • Green\textcolor{green}{Green} part is observation model, describing the likelihood of making an observation zz given the state xx
  • GP-BayesFilter improves these two parts.

The dynamics model maps the state and control (xt,ut)(x_t,u_t) to the state transition Δxt=xt+1xt\Delta x_t = x_{t+1} - x_t. So, the training data is

Dp=<(X,U),X>D_p = <(X,U),X'>

The observation model maps from the state xtx_t to the observation ztz_t. So, the training data is

Do=<X,Z>D_o = <X,Z>

The resulting GP dynamics and observation models are (p.44)

p(xtxt1,ut1)N(GPμ([xt1,ut1],Dp),GPΣ([xt1,ut1],Dp))p(x_t|x_{t-1},u_{t-1}) \approx \mathcal{N}(\text{GP}_\mu([x_{t-1},u_{t-1}],D_p), \text{GP}_\Sigma([x_{t-1},u_{t-1}],D_p))


p(ztxt)N(GPμ(xt,Do),GPΣ(xt,Do))p(z_t|x_t) \approx \mathcal{N}(\text{GP}_\mu(x_t,D_o), \text{GP}_\Sigma(x_t,D_o))