Meta-Learning-Based Safety-Critical Control in Multi-Obstacles Environments

Autonomous robots operating in diverse scenarios are expected to safely and efficiently adapt to new, unknown, and cluttered environments. In this paper, we introduce a real-time goal-seeking and exploration framework incorporating novel meta-signed distance functions (MetaSDFs) and meta-buffer robust control barrier functions (Meta-BRCBFs). To adapt to environmental changes in real time, we employ Bayesian meta-learning to construct MetaSDFs. Deep neural network weights are initially trained offline, followed by efficient online adaptation at the last Bayesian layer, allowing for online updates at linear time complexity. Each MetaSDF is individually trained for its corresponding obstacle class, enhancing online distance estimation accuracy. Subsequently, buffer zones are constructed around the MetaSDFs to establish corresponding Meta-BRCBFs. These Meta-BRCBFs are activated only when the robot enters these zones, substantially reducing the number of CBFs required. Outside these specified buffer zones, the robot remains in goal-seeking mode, focusing on task completion. After entering a buffer zone, it transitions to exploration mode, prioritizing safety and exploring safe pathways, effectively balancing task execution with environmental adaptability. We demonstrate that, under this framework, the system achieves both safety and asymptotic stabilization. Extensive simulations and experiments are conducted to demonstrate our framework’s effectiveness in both simulated scenarios and real-world environments. These tests confirm our framework’s real-time capabilities and safety assurances in dynamic settings where state-of-the-art methods fail.