DART: Learning-Enhanced Model Predictive Control for Dual-Arm Non-Prehensile Manipulation

arXiv cs.RO / 4/21/2026

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Key Points

The paper introduces DART, a new dual-arm framework for non-prehensile tray manipulation that targets precise object repositioning for service robotics scenarios like hotels and hospitality.
DART combines nonlinear Model Predictive Control (MPC) with an optimization-based impedance controller to control object motion relative to a dynamically controlled tray.
It compares three approaches to modeling tray–object dynamics inside the MPC: a physics-based analytical model, an online regression-based identification model, and a reinforcement learning-based dynamics model that generalizes across object properties.
The authors validate the system in simulation across objects with different mass, geometry, and friction, reporting performance trade-offs among modeling strategies in settling time, steady-state error, control effort, and generalization.

Abstract

What appears effortless to a human waiter remains a major challenge for robots. Manipulating objects nonprehensilely on a tray is inherently difficult, and the complexity is amplified in dual-arm settings. Such tasks are highly relevant to service robotics in domains such as hotels and hospitality, where robots must transport and reposition diverse objects with precision. We present DART, a novel dual-arm framework that integrates nonlinear Model Predictive Control (MPC) with an optimization-based impedance controller to achieve accurate object motion relative to a dynamically controlled tray. The framework systematically evaluates three complementary strategies for modeling tray-object dynamics as the state transition function within our MPC formulation: (i) a physics-based analytical model, (ii) an online regression based identification model that adapts in real-time, and (iii) a reinforcement learning-based dynamics model that generalizes across object properties. Our pipeline is validated in simulation with objects of varying mass, geometry, and friction coefficients. Extensive evaluations highlight the trade-offs among the three modeling strategies in terms of settling time, steady-state error, control effort, and generalization across objects. To the best of our knowledge, DART constitutes the first framework for non-prehensile dual-arm manipulation of objects on a tray. Project Link: https://dart-icra.github.io/dart/