Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions

Sarah Dean\(^1\)    Andrew J. Taylor\(^2\)    Ryan K. Cosner\(^2\)

Benjamin Recht\(^1\)    Aaron D. Ames\(^2\)

\(^1\)UC Berkeley    \(^2\)Caltech

Motivation: Sensing

Robotic systems use increasingly complex sensors

Motivation: Safety

Complex calibration problems—opportunities for measurement model error

Motivation: Safety

Complex calibration procedures—opportunities for measurement model error

How do we ensure that complex and data-driven sensing can be safe and effective for controlling robotic systems?

Problem setting

Nonlinear control affine dynamics:  \(\dot{\mathbf x} = \mathbf f(\mathbf x)+\mathbf g(\mathbf x)\mathbf u\)

System observed by  \(\mathbf p(\mathbf x) = \mathbf y\), state estimated by \(\widehat{ \mathbf q}(\mathbf y) = \widehat{\mathbf x}\)

Subset of state space:

\(\mathcal C = \{\mathbf x \mid h(\mathbf x) \geq 0\}\)

Safety defined as invariance of \(\mathcal C\)

System observed by  \(\mathbf p(\mathbf x) = \mathbf y\), state estimated by \(\widehat{ \mathbf q}(\mathbf y) = \widehat{\mathbf x}\)

\(\widehat{\mathbf q}\)

Preliminaries: Safety via CBF

The function \(h\) is a control barrier function if for some \(\alpha\in\mathcal K_\infty^e\)

This condition ensures the existence of \(\mathbf u\) such that

\(\frac{\partial h}{\partial \mathbf x}( \mathbf x)  \dot{ \mathbf x}  \geq -\alpha(h(\mathbf x))\)

\(\sup\limits_{\mathbf u\in\mathbb{R}^m} \frac{\partial h}{\partial \mathbf x}( \mathbf x) (\mathbf f( \mathbf x) + \mathbf g(\mathbf x)\mathbf u)  \geq -\alpha(h(\mathbf x))\)

If \(\mathbf u\) chosen such that \(\dot{h}(\mathbf x, \mathbf u) \geq -\alpha( h(\mathbf x))\) then \(h(\mathbf x)\) remains nonnegative, so the safe set \(\mathcal C\) is invariant

\(\dot h( \mathbf x, \mathbf u)  \geq -\alpha(h(\mathbf x))\)

 (Ames et al. 2014, 2017)

Preliminaries: Safety via CBF

If \(\mathbf u\) chosen such that \(\dot{h}(\mathbf x, \mathbf u) \geq -\alpha( h(\mathbf x))\) then \(h(\mathbf x)\) remains nonnegative, so the safe set \(\mathcal C\) is invariant

The set of inputs which ensure safety:

\(K_\mathrm{cbf}(\mathbf x) = \{\mathbf u\mid \frac{\partial h}{\partial \mathbf x}( \mathbf x)  \mathbf f( \mathbf x) + \frac{\partial h}{\partial \mathbf x}( \mathbf x)\mathbf g(\mathbf x)\mathbf u  \geq -\alpha(h(\mathbf x))\}\)

To filter a given controller \(\mathbf k_d({\mathbf x})\) via convex optimization:

\(\argmin\limits_{\mathbf u \in \mathbb{R}^m} \| \mathbf u - \mathbf k_d({\mathbf x}) \|\)

\(~~~~~~\text{s.t.} ~~L_{\mathbf f}h( \mathbf x) + L_{\mathbf g}h(\mathbf x)\mathbf u  \geq -\alpha(h(\mathbf x))\)

Measurement Model Error

Estimated state reconstructed by approximate inverse

\(\widehat{\mathbf x} = \widehat{\mathbf q}(\mathbf y)=\mathbf x + (\widehat{\mathbf q}(\mathbf y) - {\mathbf q}(\mathbf y))=\mathbf x + {\mathbf e}(\mathbf y) \)

Our setting has imperfect state observation

Considering all \(\mathbf x\) in uncertainty set:

\(\min\limits_{\mathbf x\in \mathcal X(\mathbf y)} L_{\mathbf f} h(\mathbf x) + L_{\mathbf g}h(\mathbf x) \mathbf u + \alpha(h(\mathbf x)) \geq 0\)

\(\min\limits_{\|\mathbf e\|\leq \epsilon(\mathbf y)} L_{\mathbf f} h(\widehat{\mathbf x}-\mathbf e) + L_{\mathbf g}h(\widehat{\mathbf x}-\mathbf e) \mathbf u + \alpha(h(\widehat{\mathbf x}-\mathbf e)) \geq 0\)

 (Clark 2020, Nilsson 2020)

Measurement-Robust CBF

We define the set of inputs which ensure robust safety:

\(K_\mathrm{mr}(\mathbf y) = \{\mathbf u\mid L_{\mathbf f}h( \widehat{\mathbf x}) + L_{\mathbf g}h(\widehat{\mathbf x})\mathbf u - (a(\mathbf y) + b(\mathbf y)\|\mathbf u\|)  \geq -\alpha(h(\widehat{\mathbf x}))\}\)

for given \(a, b~:~\mathbb{R}^k\to\mathbb{R}_+\).

Main Result: As long as

  • perception errors are bounded by \(\epsilon(\mathbf y)\)
  • and \(L_{\mathbf f}h\), \(L_{\mathbf g}h\), and \(\alpha\circ h\) are Lipschitz,

then by setting \(a(\mathbf y) = (\mathcal L_{L_{\mathbf f}h}+\mathcal L_{\alpha \circ h})\epsilon(\mathbf y)\) and \(b(\mathbf y) =\mathcal L_{L_{\mathbf g}h} \epsilon(\mathbf y)\),

any controller contained in \(K_\mathrm{mr}(\mathbf y)\) renders the system safe.

Feasibility of MR-CBF

The MR-CBF optimization problem (SOCP)

is feasible as long as

\(\epsilon(\mathbf y) \leq \max \Big( \frac{\|L_{\mathbf g}h(\widehat{\mathbf x})\|}{\mathcal L_{L_{\mathbf g}h} },\frac{L_{\mathbf f}h( \widehat{\mathbf x}) +\alpha(h(\widehat{\mathbf x}))}{\mathcal L_{L_{\mathbf f}h}+\mathcal L_{\alpha \circ h}}\Big)\)

which can be achieved from data

\(\argmin\limits_{\mathbf u \in \mathbb{R}^m} \| \mathbf u - \mathbf k_d(\widehat{\mathbf x}) \|\)

\(~~~~~~~~\text{s.t.}~~L_{\mathbf f}h( \widehat{\mathbf x}) + L_{\mathbf g}h(\widehat{\mathbf x})\mathbf u\)

\(- ((\mathcal L_{L_{\mathbf f}h}+\mathcal L_{\alpha \circ h})\epsilon(\mathbf y) + \mathcal L_{L_{\mathbf g}h} \epsilon(\mathbf y)\|\mathbf u\|)  \geq -\alpha(h(\widehat{\mathbf x}))\)

Simulation Setting

Segway robot constrained to planar motion

Safety defined by pitch angle and rate

\(|\dot{\theta}_y +10(\theta_y-\theta_y^\mathrm{eq}) |\leq 4\)

Pitch angle \(\theta_y\) and position \(r\) measured only by camera

Learned Perception Module

Training data collected from grid of \(\theta_y\) and \(r\) (\(N=800\))

Model mapping image to \(\theta_y,r\) trained using sklearn kernel ridge regression with radial basis functions

Simulated Trajectories

Filtered PD control from 15 kHz camera feed

CBF filter does not ensure safety, MR-OP filter does

Conclusion

  1. MR-CBF ensures robust safety
  2. MR-CBF can be used as a safety filter via SOCP
  3. Depends on system continuity and bounded perception errors

For details, see our paper at https://arxiv.org/abs/2010.16001

Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions

Sarah Dean    Andrew J. Taylor    Ryan K. Cosner    Benjamin Recht    Aaron D. Ames

References

  1. Ames, Aaron D., Jessy W. Grizzle, and Paulo Tabuada. "Control barrier function based quadratic programs with application to adaptive cruise control." IEEE CDC, 2014.

  2. Ames, Aaron D., et al. "Control barrier function based quadratic programs for safety critical systems." IEEE TAC, 2016.

  3. Nilsson, Petter, and Aaron D. Ames. "Lyapunov-like conditions for tight exit probability bounds through comparison theorems for sdes." IEEE ACC, 2020.

  4. Clark, Andrew. "Control barrier functions for complete and incomplete information stochastic systems." IEEE ACC, 2019.

Measurement-Robust Control Barrier Functions

By Sarah Dean

Measurement-Robust Control Barrier Functions

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