Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teaming

2026-04-21

AI Summary

This work addresses the common decoupling of task scheduling and motion planning in human-robot collaboration, which often stems from the difficulty of jointly modeling users’ individualized spatiotemporal behaviors, thereby limiting both efficiency and safety. To bridge this gap, the authors propose RAPIDDS—a novel framework that unifies task-level scheduling and motion-level planning for the first time. RAPIDDS adaptively learns personalized user spatiotemporal preferences through iterative interactions and leverages a diffusion model to jointly optimize robotic trajectories. This approach enables coherent modeling and co-optimization of human spatiotemporal characteristics. Comprehensive evaluations—including simulations, real-world experiments on a 7-DoF robotic arm, and a user study with 32 participants—demonstrate that RAPIDDS significantly outperforms non-adaptive baselines across multiple objective and subjective metrics, including collaboration efficiency, proximity, fluency, and user preference.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Human-Robot Interaction
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

Effective human-robot teaming is crucial for the practical deployment of robots in human workspaces. However, optimizing joint human-robot plans remains a challenge due to the difficulty of modeling individualized human capabilities and preferences. While prior research has leveraged the multi-cycle structure of domains like manufacturing to learn an individual's tendencies and adapt plans over repeated interactions, these techniques typically consider task-level and motion-level adaptation in isolation. Task-level methods optimize allocation and scheduling but often ignore spatial interference in close-proximity scenarios; conversely, motion-level methods focus on collision avoidance while ignoring the broader task context. This paper introduces RAPIDDS, a framework that unifies these approaches by modeling an individual's spatial behavior (motion paths) and temporal behavior (time required to complete tasks) over multiple cycles. RAPIDDS then jointly adapts task schedules and steers diffusion models of robot motions to maximize efficiency and minimize proximity accounting for these individualized models. We demonstrate the importance of this dual adaptation through an ablation study in simulation and a physical robot scenario using a 7-DOF robot arm. Finally, we present a user study (n=32) showing significant plan improvement compared to non-adaptive systems across both objective metrics, such as efficiency and proximity, and subjective measures, including fluency and user preference. See this paper's companion video at: https://youtu.be/55Q3lq1fINs.

Problem

• human-robot teaming
• spatio-temporal adaptation
• multi-cycle learning
• task-motion planning
• individualized modeling

Innovation

• multi-cycle adaptation
• spatio-temporal modeling
• human-robot teaming
• diffusion-based motion planning
• joint task-motion adaptation