Language-Driven Closed-Loop Grasping with Model-Predictive Trajectory Replanning
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914840989138944 |
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| author | Nguyen, Huy Hoang Vu, Minh Nhat Beck, Florian Ebmer, Gerald Nguyen, Anh Kugi, Andreas |
| author_facet | Nguyen, Huy Hoang Vu, Minh Nhat Beck, Florian Ebmer, Gerald Nguyen, Anh Kugi, Andreas |
| contents | Combining a vision module inside a closed-loop control system for a \emph{seamless movement} of a robot in a manipulation task is challenging due to the inconsistent update rates between utilized modules. This task is even more difficult in a dynamic environment, e.g., objects are moving. This paper presents a \emph{modular} zero-shot framework for language-driven manipulation of (dynamic) objects through a closed-loop control system with real-time trajectory replanning and an online 6D object pose localization. We segment an object within $\SI{0.5}{\second}$ by leveraging a vision language model via language commands. Then, guided by natural language commands, a closed-loop system, including a unified pose estimation and tracking and online trajectory planning, is utilized to continuously track this object and compute the optimal trajectory in real-time. Our proposed zero-shot framework provides a smooth trajectory that avoids jerky movements and ensures the robot can grasp a non-stationary object. Experiment results exhibit the real-time capability of the proposed zero-shot modular framework for the trajectory optimization module to accurately and efficiently grasp moving objects, i.e., up to \SI{30}{\hertz} update rates for the online 6D pose localization module and \SI{10}{\hertz} update rates for the receding-horizon trajectory optimization. These advantages highlight the modular framework's potential applications in robotics and human-robot interaction; see the video in https://www.acin.tuwien.ac.at/en/6e64/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09039 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Language-Driven Closed-Loop Grasping with Model-Predictive Trajectory Replanning Nguyen, Huy Hoang Vu, Minh Nhat Beck, Florian Ebmer, Gerald Nguyen, Anh Kugi, Andreas Robotics Combining a vision module inside a closed-loop control system for a \emph{seamless movement} of a robot in a manipulation task is challenging due to the inconsistent update rates between utilized modules. This task is even more difficult in a dynamic environment, e.g., objects are moving. This paper presents a \emph{modular} zero-shot framework for language-driven manipulation of (dynamic) objects through a closed-loop control system with real-time trajectory replanning and an online 6D object pose localization. We segment an object within $\SI{0.5}{\second}$ by leveraging a vision language model via language commands. Then, guided by natural language commands, a closed-loop system, including a unified pose estimation and tracking and online trajectory planning, is utilized to continuously track this object and compute the optimal trajectory in real-time. Our proposed zero-shot framework provides a smooth trajectory that avoids jerky movements and ensures the robot can grasp a non-stationary object. Experiment results exhibit the real-time capability of the proposed zero-shot modular framework for the trajectory optimization module to accurately and efficiently grasp moving objects, i.e., up to \SI{30}{\hertz} update rates for the online 6D pose localization module and \SI{10}{\hertz} update rates for the receding-horizon trajectory optimization. These advantages highlight the modular framework's potential applications in robotics and human-robot interaction; see the video in https://www.acin.tuwien.ac.at/en/6e64/. |
| title | Language-Driven Closed-Loop Grasping with Model-Predictive Trajectory Replanning |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.09039 |