Deep reinforcement learning agents achieve superhuman control but frequently violate physical safety constraints during exploration; control barrier functions and control-affine dynamics provide mathematically certifiable safety guarantees for autonomous robotics.
Deep reinforcement learning enables autonomous quadcopters and robotic manipulators to master agile, complex maneuvers through trial-and-error interaction with physical environments.
However, standard neural network exploration is dangerous: an autonomous car or industrial robotic arm learning via unconstrained reinforcement learning will inevitably crash into walls or pedestrians during training.
This robotics engineering paper develops a certifiable model-based reinforcement learning framework incorporating control-affine system dynamics and Control Barrier Functions (CBFs). By projecting policy actions onto forward-invariant safe sets, the framework guarantees that the system never breaches safety boundaries during learning.
Mathematical safety certificates remove the primary barrier preventing the deployment of deep reinforcement learning in real-world autonomous vehicles, surgical robotics, and commercial drone delivery.
Certifiable Safe Model-Based Reinforcement Learning with Control-Affine Dynamics Approximation
Safe model-based reinforcement learning (RL) often bridges control-theoretic analysis and RL for robots to safely explore (partially) unknown system dynamics while deriving control actions for task efficiency. The control performance and safety assurance typically rely on prior knowledge of partially modeled nominal system dynamics and the data-driven models that compensate for residual model uncertainties. However, existing methods often overlook the structure of residual model uncertainties (e.g., components affine in control), which could lead to overly conservative robot behaviors or invalid safety guarantees under the safe learning-based controllers. This paper proposes a safe reinforcement learning framework that learns control-affine dynamics with a certifiable data-driven safe policy using control barrier functions (CBF). Specifically, we first use Control-Affine Random Fourier Features (ARFF) to model robot dynamics in a control-affine form, which offers computational efficiency that scales with dataset size and reduces potential model bias for model-based reinforcement learning. Then, a model-free, efficient uncertainty quantification method using adaptive conformal prediction (ACP) is applied to quantify the uncertainty in the safety constraint arising from the learned control-affine dynamics. This allows for data-driven safety assurance amenable to principled and efficient controller synthesis with CBF. Simulation results on the cartpole and the 3D quadrotor platforms demonstrate the effectiveness of the proposed framework.
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