Robust Policy Control Barrier Functions

Robust Policy CBFs: Constructing robust CBFs from the Policy Value Function

In this work, we leverage the insight that the maximum-over-time constraint function is a CBF for any choice of rollout policy $\pi$ . The policy value function for a policy $\pi$ is defined as

\displaystyle V_\infty^{h,\pi}(x) \coloneqq \sup_{t \geq 0}\, h(x_t^\pi)

where the avoid set $\mathcal{A}$ is described as the superlevel set of some continuous constraint function $h$ :

\displaystyle \mathcal{A} = \{ x \mid h(x) > 0 \}

$V_\infty^{h,\pi}$ contains knowledge about the invariant set, which can be used to render a (potentially unsafe) nominal policy $\pi_\mathrm{nom}$ safe via a safety filter framework. However, deriving $V_\infty^{h,\pi}$ over the infinite horizon is computationally intractable. Although an approximation of the policy value function $V_\infty^{h,\pi}$ can be learned [1] , it requires certifying the neural network as a valid CBF and limits the interpretability. Furthermore, it does not consider uncertainties in the system dynamics. Therefore, we present a practical approximation of Robust Policy CBFs.

We define the robust policy value function equivalent of the above introduced value function as

\displaystyle V_\infty^{h,\pi}(x) \coloneqq \sup_{t \geq 0}\, \sup_{d(\cdot)} h(x_t^\pi)

Since this formulation is computationally intractable, we propose a practical (finite-time and sampling-based) approximation:

\displaystyle V_{T,N}^{h,\pi}(x_0) \coloneqq \max_{i = 1,\ldots,N}\sup_{0 \leq t < T} h(x_t^i)

More details can be found in Finite-Time Approximation (of Infinite Time), Sampling-based Approximation (of Worst-Case Disturbance) or in the paper.

Simulation Experiments

To assess the safety improvements brought about by the proposed RPCBF, we integrate it with Shield-MPPI [2] and evaluate the performance on AutoRally.

MPPI

Shield-MPPI-RPCBF

Hardware Experiments

We conduct hardware experiments on the Crazyflie platform to determine whether the proposed RPCBF can be robust to disturbances encountered in the real world. The error between a simple double integrator model and the true dynamics is treated as an acceleration disturbance. We randomly generate an unsafe nominal trajectory which intersects with the cylindrical obstacle and is tracked using the onboard PID controller. The proposed RPCBF safety filter runs at 100 Hz.

PCBF Safety Filter enters Obstacle

RPCBF Safety Filter is safe

Finite-Time Approximation (of Infinite Time)

To enable numerical evaluation of the value function, we consider a finite horizon approximation of the infinite-horizon value function. For horizon $T$ , we use the following approximation.

\begin{aligned} V_\infty^{h,\pi}(x_0) &\coloneqq \max \left\{ \sup_{0 \leq t < T}\, h(x_t^\pi),\; V_\infty^{h,\pi}(x_T) \right\} \\[1.2em] &\approx \underbrace{\sup_{0\leq t < T} h(x_t^\pi)}_{\coloneqq V^{h,\pi}_T(x_0) } \end{aligned}

\begin{aligned} &\mathrel{\phantom{=}} V_\infty^{h,\pi}(x_0) \\ &\coloneqq \max \left\{ \sup_{0 \leq t < T}\, h(x_t^\pi),\; V_\infty^{h,\pi}(x_T) \right\} \\[1.2em] &\approx \underbrace{\sup_{0\leq t < T} h(x_t^\pi)}_{\coloneqq V^{h,\pi}_T(x_0) } \end{aligned}

In particular, we prove that given a sufficiently long horizon $T$ , the finite-time approximation $V^{h,\pi}_T(x_0)$ is a valid CBF. See Corollary 1 in the paper for more details.

Note: The finite-horizon truncation here is closely related to the truncation used by practitioners of MPC. Namely, recursive feasibility of MPC often requires a terminal constraint set, yet this is rarely used in practice. See the paper for more details.

Sampling-Based Approximation (of Worst-Case Disturbance)

We further introduce a sampling-based approximation of the worst-case disturbance over the finite horizon. Instead of performing the intractable optimization over all disturbances, we sample $N$ disturbances and evaluate the worst-case disturbance trajectory.

\displaystyle V_{T,N}^{h,\pi}(x_0) \coloneqq \max_{i = 1,\ldots,N}\sup_{0 \leq t < T} h(x_t^i)

For simplicity, we sample the disturbances uniformly for each timestep. While this does not perform exact minimization, we find that this performs well in practice.

While we do not explore this here, other optimizers can be used to better approximate the worst-case disturbances. We leave this as future work.

Supplementary Video

Related Works

1. Oswin So, Zachary Serlin, Makai Mann, Jake Gonzales, Kwesi Rutledge, Nicholas Roy, and Chuchu Fan, "How to Train Your Neural Control Barrier Function: Learning Safety Filters for Complex Input-Constrained Systems", IEEE International Conference on Robotics and Automation (ICRA) , 2024
2. Ji Yin, Charles Dawson, Chuchu Fan, and Panagiotis Tsiotras, "Shield model predictive path integral: A computationally efficient robust MPC method using control barrier functions", IEEE Robotics and Automation Letters , 2023

Abstract

Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the Robust Policy CBF (RPCBF), a practical method of constructing CBF approximations that is easy to implement and robust to disturbances via the estimation of a value function. We demonstrate the effectiveness of our method in simulation on a variety of high relative degree input-constrained systems. Finally, we demonstrate the benefits of RPCBF in compensating for model errors on a hardware quadcopter platform by treating the model errors as disturbances.