Sarah Dean PRO
asst prof in CS at Cornell
Certainty Equivalent Perception-Based Control
Sarah Dean and Benjamin Recht
UC Berkeley
L4DC, 8 June 2021
Motivation
Machine learning is a promising avenue for incorporating rich sensing modalities
Can we make strong guarantees in these settings?
backup slide about EKF/modelling vs. end-to-end
Perception-based optimal control
\(\displaystyle \min_{\pi\in\Pi} ~~\mathrm{cost}(x_0,u_0,x_1,\dots)\)
\(~~~\mathrm{s.t.}~~ u_t = \pi_t(z_{0:t})\)
\(~~~~~~~~~~x_{t+1} = \mathrm{dynamics}_t(x_t,u_t)\)
\(~~~~~~~~~~z_t = \mathrm{observation}_t(Cx_t)~\)
observation
?
?
\(\mathrm{dynamics~\&}\)\(\mathrm{observation}\)
\(\pi\)
\(z_t\)
\(u_t\)
\(x_t\)
output
\(y_t=Cx_t\)
Observation-feedback optimal control problem
Perception-based optimal control
\(\displaystyle \min_{\pi\in\Pi} ~~\mathrm{cost}(\widehat x_0,u_0,\widehat x_1,\dots)\)
\(~~~\mathrm{s.t.}~~ u_t = \pi_t(\widehat x_{0:t})\)
\(~~~~~~~~~~\widehat x_{t+1} = \mathrm{dynamics}_t(\widehat x_t,u_t)\)
\(~~~~~~~~~~\widehat x_t =\mathrm{EKF}(z_{0:t})~\)
\(\mathrm{dynamics,}\)\(\mathrm{obs, EKF}\)
\(\pi\)
\(\widehat x_t\)
\(u_t\)
\(x_t\)
Classic approach: physical models and filtering
Perception-based optimal control
\(\displaystyle \min_{\pi\in\Pi} ~~\mathrm{cost}(x_0,u_0,x_1,\dots)\)
\(~~~\mathrm{s.t.}~~ u_t = \widehat \pi_t(z_{0:t})\)
?
\(\mathrm{dynamics~\&}\)\(\mathrm{observation}\)
\(\widehat \pi\)
\(z_t\)
\(u_t\)
\(x_t\)
End-to-end approach: learn everything from data
Perception-based optimal control
\(\displaystyle \min_{\pi\in\Pi} ~~\mathrm{cost}(x_0,u_0,x_1,\dots)\)
\(~~~\mathrm{s.t.}~~ u_t = \pi_t(y_{0:t})\)
\(~~~~~~~~~~x_{t+1} = \mathrm{dynamics}_t(x_t,u_t)\)
\(~~~~~~~~~~y_t = \mathrm{perception}(\mathrm{observation}_t(Cx_t))~\)
\(\mathrm{dynamics~\&}\)\(\mathrm{obs,~percept.}\)
\(\pi\)
\(y_t\)
\(u_t\)
\(x_t\)
Our focus: learned perception map
Problem setting
\(\text{s.t.}~~x_{t+1} = {A }x_t+ {B} u_t\)
Robust reference tracking with linear dynamics and nonlinear partial observation
\(z_{t} =g(Cx_t)\)
\(\displaystyle\mathrm{cost} = \sup_{\substack{t\geq 0\\\mathbf x^\mathrm{ref} \in \mathcal R\\ \|x_0\|\leq \sigma_0}}\left\|\begin{bmatrix} Q (x_t - x_t^\mathrm{ref})\\ Ru_t \end{bmatrix}\right\|_\infty\)
Assumption 1:
\(A,B,C\) and \(Q,R\) are known and well posed
Assumption 2:
\(\mathcal R\) encodes a bounded radius of operation
Assumption 3:
Invertible \(h(g(y)) = y\) and \(g,h\) continuous
\(\displaystyle \min_{\pi}\)
\(\displaystyle \min_{\mathbf K}\)
\(u_{t} =\pi(z_{0:t}, x^\mathrm{ref}_{0:t})\)
Assumption 4:
Noisy training signal \(y^\mathrm{train}_{t} =Cx_t+\eta_t\)
\(y_t = h(z_t) = Cx_t\)
\(u_t = \mathbf K(y_{0:t}, x^\mathrm{ref}_{0:t})\)
Certainty equivalent controller \(\widehat \pi(z_{0:t}, x^\mathrm{ref}_{0:t}) = \mathbf K_\star (\widehat h(z_{0:t}), x^\mathrm{ref}_{0:t}) \)
where \(\widehat h\) is learned from data
Transform to linear output feedback problem with \(h\)
\(\pi_\star(z_{0:t}, x^\mathrm{ref}_{0:t}) = \mathbf K_\star (h(z_{0:t}), x^\mathrm{ref}_{0:t}) \)
Assumption 3 applies when:
- each position results in a unique image
- small changes to position result in small changes to image
\(\mathrm{dynamics~\&}\)\(\mathrm{observation}\)
\(\mathbf K\)
\(z_t\)
\(u_t\)
\(x_t\)
\(y_t\)
\(\mathrm{linear}\)
\(\mathrm{dynamics}\)
\(\mathbf K\)
\(y_t\)
\(u_t\)
\(x_t\)
\(h\)
Main Result (Informal)
\(\mathrm{cost}(\widehat\pi) - \mathrm{cost}(\pi_\star) \lesssim\) \( L \) \(r_\star\) \(s_\star\) \( \left(\frac{\sigma}{T}\right)\)\({}^{\frac{1}{p+4}}\)
depending on the continuity of \(g\) and \(h\), the radius of operation, the sensitivity of the optimal controller, the sensor noise, amount of data, and the dimension of the output
Ingredients
The certainty-equivalent controller has bounded suboptimality w.h.p.
1. Uniform convergence of \(\widehat h\)
- sampling scheme
2. Closed-loop performance
Related Work
Classic controls: Weiner system identification
- SISO (Greblicki et al. 97) or restrictive assumptions (Westwick & Verhaegen 96; Janczak et al. 07)
- Focus on ID rather than control performance
Recent work:
Block MDP (Misra et al. 2020) and Rich LQR (Mhamedi et al. 2020) settings
- Sample complexity has polynomial dependence
- Finite state space or horizon sidestep stability concerns
Necessity of pointwise bounds
Example: 1D unstable linear system with arbitrary linear controller
\(x_{t+1} = a x_t + u_t\qquad u_t = \mathbf{K}(x^\mathrm{ref}_{0:t},\widehat h(z_{0:t}))\)
near perfect perception map: \(\widehat h(g(x)) = \begin{cases} 0 & x = \bar x,~ |x|>r\\ x &\text{otherwise} \end{cases}\)
There exists a reference signal contained in \([-r,r]\) that causes the system to pass through \(\bar x\) and subsequently go unstable
\(\bar x\)
\(r\)
\(-r\)
\(t\)
Nonparametric regression
Nadarya Watson Regression: from training data \(\{(z_t, y_t^\mathrm{train})\}_{t=0}^T\)
predictions are weighted averages,
\(\displaystyle \widehat h(z) = \sum_{t=0}^T \frac{k_\gamma(z_t, z)}{\sum_{\ell=0}^T k_\gamma(z_\ell, z)} y_t^\mathrm{train} \)
Theorem (uniform convergence): Suppose training data uniformly sampled from \( \{y\mid \|y\|_\infty\leq r\}\) and bandwidth \(\gamma \propto T^{\frac{1}{p+4}}\). Whenever the system state contained in \(\{x\mid\|Cx\|_\infty \leq r\}\), then w.h.p.
\(\|h(z) - \widehat h(z)\|_\infty \lesssim rL_g L_h \left(\frac{p^2\sigma_\eta^4}{T}\right)^{\frac{1}{p+4}}\)
bandwidth
Nonparametric regression
Nadarya Watson Regression: from training data \(\{(z_t, y_t^\mathrm{train})\}_{t=0}^T\)
\(\displaystyle \widehat h(z) = \sum_{t=0}^T \frac{k_\gamma(z_t, z)}{s_T(z)} y_t^\mathrm{train} \)
\(=\sum_{t=0}^T k_\gamma(z_t, z)\)
The kernel function has the form \(\kappa\left(\frac{\rho(z_t, z)}{\gamma}\right)\) for
- \(\gamma\in\mathbb{R}_+\) bandwidth
- metric \(\rho:\mathbb{R}^q\to \mathbb{R}_+\)
- \(\kappa:\mathbb{R}_+\to[0,1]\) Lipschitz and bounded support
Sampling with linear control
Drive system to uniform samples \(y_\ell^\mathrm{ref}\) using training output \(y_t^\mathrm{train}\)
\(\mathbf K\)
\(y^\mathrm{ref}_\ell \sim \mathrm{Unif}\{|y|\leq r\}\)
\(\mathrm{dynamics~\&}\)\(\mathrm{observation}\)
\(z_t\)
\(u_t\)
\(x_t\)
\(y_t^\mathrm{train}\)
How to achieve uniform sampling?
Closed-loop guarantees
How does imperfect perception affect system evolution?
Define errors \(e_t = \widehat h(z_t) - h(z_t) = \widehat h(z_t) - Cx_t\)
\(\displaystyle u_t = \sum_{k=0}^t K_k^y \widehat h(z_{t-k}) + K_k^\mathrm{ref} x^\mathrm{ref}_{t-k}\)
\(\displaystyle x_{t+1}=Ax_t+Bu_t\)
\(\displaystyle u_t = \sum_{k=0}^t K_k^y Cx_{t-k} + K_k^y Ce_{t-k} + K_k^\mathrm{ref} x^\mathrm{ref}_{t-k}\)
\(x_t = \sum_{k=0}^t \Phi_{xe}(k) e_{t-k} + \Phi_{xr}(k) x^\mathrm{ref}_{t-k}\)
Linearly.
\(u_t = \sum_{k=0}^t \Phi_{ue}(k) e_{t-k} + \Phi_{ur}(k) x^\mathrm{ref}_{t-k}\)
Proposition: Suppose that perception errors are uniformly bounded by \(\varepsilon_h\) and let \(\mathbf \Phi\) be system response associated with \(\mathbf K_\star\). Then,
\(\mathrm{cost}(\widehat\pi) \leq \mathrm{cost}(\pi_\star) + \varepsilon_h ~\left\|\begin{bmatrix} Q\mathbf \Phi_{xe}\\ R\mathbf \Phi_{ue} \end{bmatrix}\right\|_{\mathcal L_1} \)
Main Result
\(\mathrm{cost}(\widehat\pi) - \mathrm{cost}(\pi_\star) \lesssim\) \(rL_g L_h \left(\frac{p^2\sigma_\eta^4}{T}\right)^{\frac{1}{p+4}}\) \(\left\|\begin{bmatrix} Q\mathbf \Phi_{xn}\\ R\mathbf \Phi_{un} \end{bmatrix}\right\|_{\mathcal L_1} \)
The certainty-equivalent controller has bounded suboptimality w.h.p.
Ingredients
1. Uniform convergence of \(\widehat h\)
bounded errors
2. Closed-loop performance
propagation of errors
Simulation Experiments
Simplified UAV model: 2D double integrator
\(x_{t+1} = \begin{bmatrix}1 & 0.1 & & \\ 0 & 1 & & \\ & & 1 & 0.1 \\ & & 0 & 1\end{bmatrix} x_t +\begin{bmatrix}0 & \\ 1 & \\ & 0 \\ & 1 \end{bmatrix} u_t \)
\(y_t = \begin{bmatrix} 1 & 0 & & \\ & & 1 & 0\end{bmatrix} x_t\)
\(z_t\) from CARLA simulator
Data-driven perception
Data collected with linear control and periodic reference signal:
Nadarya Watson (NW) with kernel \(k_\gamma(z, z_t) = \mathbf{1}\{\|z-z_t\|_2 \leq \gamma\}\)
Kernel Ridge Regression (KRR) with radial basis functions
Visual Odometry (VO) matches \(z\) to some \(z_t\) in database of labelled training images, uses homography between images to estimate pose
Simultaneous Localization and Mapping (SLAM) like VO with memory: adds new observations to database online, and initializes estimates based on previous timestep
Perception errors
classic nonparametric methods look similar
memoryless classic computer vision is similar, if noisier/wider
very different!
building obstructs view
Thanks for your attention!
Certainty-Equivalent Perception-Based Control
Sarah Dean and Benjamin Recht
Read more at arxiv.org/abs/2008.12332
Code at github.com/modestyachts/certainty_equiv_perception_control
Certainty Equivalent Perception-Based Control
By Sarah Dean
