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

TL;DR

This paper introduces a new framework for telepresence robots to safely navigate with humans in hospitals by predicting human motion and using safe reinforcement learning.

Contribution

The novel contribution is a modular framework combining transformer-based human-motion forecasting with a safety-aware reinforcement learning controller for telepresence robots.

Findings

The proposed method improved task success by 21.6% and reduced collisions by 47.3% compared to baselines.

Median minimum human–robot clearance increased by 0.19 m and near-miss events decreased by 38.5%.

Ablation studies showed that removing forecasting or the CBF shield significantly degraded performance and safety.

Abstract

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.

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.

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.

Anticipatory perception combined with Safe-RL yields substantially safer and more reliable telepresence co-navigation in human-dense clinical layouts without sacrificing efficiency. The framework is modular, enabling alternative forecasters and safety shields. Limitations include sensitivity to forecast drift during abrupt changes in crowd flow. Future work will explore on-device adaptation, shared-autonomy overlays to incorporate operator intent, and prospective evaluations in live hospital workflows.

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