A Learned Policy Is Not A Safety Case
A goal-seeking policy can be effective on average and still issue one command that crosses an obstacle boundary.
safepathshield wraps nominal velocity with an analytical control-barrier projection: unchanged when safe, minimally corrected when the barrier inequality would be violated.
The Demo
The nominal controller records 39 collision steps. The shield intervenes 75 times, preserves 0.00137 positive minimum clearance, records zero collisions, and reaches the goal with 0.05978 error.
The fixture is a deterministic single-integrator robot with one circular obstacle. Physical deployment requires full dynamics, uncertainty, actuator limits, state estimation, and hardware validation.
Research Basis
Current work combines learned control-barrier functions with formal verification and reinforcement-learning safety layers.