CPED-NCBF uses split-conformal prediction to set safety margins in neural CBFs learned from expert demonstrations, improving simulated safety rates, but the guarantee is weakened by retraining after calibration.
Safe Legged Locomotion using Collision Cone Control Barrier Functions (C3BFs)
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abstract
Legged robots exhibit significant potential across diverse applications, including but not limited to hazardous environment search and rescue missions and the exploration of unexplored regions both on Earth and in outer space. However, the successful navigation of these robots in dynamic environments heavily hinges on the implementation of efficient collision avoidance techniques. In this research paper, we employ Collision Cone Control Barrier Functions (C3BF) to ensure the secure movement of legged robots within environments featuring a wide array of static and dynamic obstacles. We introduce the Quadratic Program (QP) formulation of C3BF, referred to as C3BF-QP, which serves as a protective filter layer atop a reference controller to ensure the robots' safety during operation. The effectiveness of this approach is illustrated through simulations conducted on PyBullet.
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CPED-NCBFs: A Conformal Prediction for Expert Demonstration-based Neural Control Barrier Functions
CPED-NCBF uses split-conformal prediction to set safety margins in neural CBFs learned from expert demonstrations, improving simulated safety rates, but the guarantee is weakened by retraining after calibration.