Trust, Sensing, and Learning for Provable Multi-Robot Performance

Stephanie Gil studies reliable coordination in robot networks facing malicious information and ordinary environmental uncertainty. Communication signals provide physical evidence of trustworthiness that is difficult to forge; the cy-trust framework turns that evidence into probabilistic trust estimates and weights neighboring agents accordingly. For consensus and distributed optimization, the analysis establishes almost-sure convergence with bounded departures from nominal performance even when malicious agents form a majority of a node’s neighbors, exceeding the classical Byzantine threshold. Theory and hardware experiments support these guarantees. For natural uncertainty, real-time sensing is incorporated into rollout-based reinforcement learning, reweighting possible futures. Applications include fleet routing under random demand and Project CETI’s autonomous robotic rendezvous with sperm whales at sea. The talk closes with directions for combining trust and long-horizon sequential decisions to retain resilience when planning data may be corrupted.