Transformer-based human-motion forecasting coupled with safe reinforcement learning for telepresence robot co-navigation

Abstract

Introduction Telepresence robots (TPRs) must co-navigate with humans in constrained hospital environments, where safety depends on anticipating rather than merely reacting to human motion. Existing approaches rarely integrate short-horizon human-motion forecasting with safety-constrained control, which reduces robustness in dense corridors and ward bays. This study addresses this gap by evaluating an anticipatory, safety-aware co-navigation framework for TPRs. Methods We developed a modular framework that couples a lightweight transformer-based forecaster that predicts multi-agent trajectories under occlusion with a safe reinforcement learning (RL) controller. The forecaster produces short-term distributions over pedestrian states that are embedded into the RL policy state and cost as risk-aware occupancy features. Safety is enforced via constrained policy optimization augmented by a run-time control barrier function (CBF) shield that filters unsafe actions. We benchmarked the approach against a social-force or dynamic window approach (DWA), an attention-based crowd-RL policy, and model predictive control (MPC) with CBF. Experiments were conducted across two hospital-like benchmarks (a crowded corridor and a four-bed ward), totaling 2,400 episodes. Outcomes included task success, collision count, minimum human-robot clearance, near-miss events ( ≤ 0.3 m), time-to-goal, CBF violations, and ablations removing forecasting and the CBF shield. Results Relative to the best-performing baseline, the proposed method improved task success by 21.6% and reduced collisions by 47.3%. Median minimum human-robot clearance increased by 0.19 m, and near-miss events decreased by 38.5%. Time-to-goal was maintained within +2.7% of MPC+CBF while incurring zero CBF violations under the shield. Ablation studies showed that removing forecasting degraded success by 14.2%, whereas removing the CBF shield increased constraint breaches from 0% to 6.1% of steps. Discussion Anticipatory perception combined with Safe-RL yields substantially safer and more reliable telepresence co-navigation in human-dense clinical layouts without sacrificing efficiency.