Inverse Safety Filtering: Inferring Constraints from Safety Filters for Decentralized Coordination
This addresses decentralized coordination for multi-agent systems where communication is limited, though it appears incremental as it builds on existing safety filter approaches.
The paper tackles the problem of safe multi-agent coordination in uncertain environments by introducing an online method to infer safety constraints from observing other agents' safety-filtered actions, proving convergence under certain conditions and demonstrating effectiveness through Monte Carlo simulations and quadruped robot hardware experiments.
Safe multi-agent coordination in uncertain environments can benefit from learning constraints from other agents. Implicitly communicating safety constraints through actions is a promising approach, allowing agents to coordinate and maintain safety without expensive communication channels. This paper introduces an online method to infer constraints from observing the safety-filtered actions of other agents. We approach the problem by using safety filters to ensure forward safety and exploit their structure to work backwards and infer constraints. We provide sufficient conditions under which we can infer these constraints and prove that our inference method converges. This constraint inference procedure is coupled with a decentralized planning method that ensures safety when the constraint activation distance is sufficiently large. We then empirically validate our method with Monte Carlo simulations and hardware experiments with quadruped robots.