DART: Learning-Enhanced Model Predictive Control for Dual-Arm Non-Prehensile Manipulation
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908993034649600 |
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| author | Das, Autrio Bollimuntha, Shreya Jeevesh, Madala Venkata Renu Patra, Keshab Ghosh, Tashmoy Govindan, Nagamanikandan Singh, Arun Kumar Krishna, K Madhava |
| author_facet | Das, Autrio Bollimuntha, Shreya Jeevesh, Madala Venkata Renu Patra, Keshab Ghosh, Tashmoy Govindan, Nagamanikandan Singh, Arun Kumar Krishna, K Madhava |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17833 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DART: Learning-Enhanced Model Predictive Control for Dual-Arm Non-Prehensile Manipulation Das, Autrio Bollimuntha, Shreya Jeevesh, Madala Venkata Renu Patra, Keshab Ghosh, Tashmoy Govindan, Nagamanikandan Singh, Arun Kumar Krishna, K Madhava Robotics 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/ |
| title | DART: Learning-Enhanced Model Predictive Control for Dual-Arm Non-Prehensile Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.17833 |