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/

翻译：对人类服务员而言轻而易举的动作，对机器人来说仍是重大挑战。在托盘上非抓取操作物体本就困难，而双臂场景更使复杂度倍增。此类任务与酒店及服务业等领域的服务机器人高度相关——机器人需精准运输并重新定位多种物体。我们提出DART这一新型双臂框架，将非线性模型预测控制（MPC）与基于优化的阻抗控制器相结合，实现对动态控制托盘上的物体运动的精确操控。该框架系统评估了三种互补策略，用于在MPC公式中建模托盘-物体动力学作为状态转移函数：（i）基于物理的分析模型；（ii）在线回归辨识模型（可实时自适应调整）；（iii）基于强化学习的动力学模型（可泛化至不同物体属性）。我们的流程在含不同质量、几何形状与摩擦系数的物体仿真中完成验证。大量评估揭示了上述三种建模策略在稳定时间、稳态误差、控制力度及跨物体泛化能力等方面的权衡特性。据我们所知，DART是首个实现托盘上物体非抓取双臂操作的系统框架。项目链接：https://dart-icra.github.io/dart/