Learning Dynamic Pick-and-Place for a Legged Manipulator
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911688050081792 |
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| author | Jung, Moonkyu Lee, Jiseong He, Zhengmao Youm, Donghoon Mun, Juhyeok Kim, HyeongJun Oh, Hyunsik Choi, Donghyuk Hur, Jungwoo Song, Jie Hwangbo, Jemin |
| author_facet | Jung, Moonkyu Lee, Jiseong He, Zhengmao Youm, Donghoon Mun, Juhyeok Kim, HyeongJun Oh, Hyunsik Choi, Donghyuk Hur, Jungwoo Song, Jie Hwangbo, Jemin |
| contents | Legged manipulators extend robotic capabilities beyond static manipulation by integrating agile locomotion with versatile arm control. However, achieving precise manipulation while maintaining coordinated locomotion remains a major challenge. This work presents a hierarchical reinforcement learning framework for dynamic pick-and-place tasks using a quadruped equipped with a 6-DOF robotic arm. The framework incorporates an explicit mass estimation module enabling adaptive whole-body control for objects with varying weights. In simulation, the system achieves an 86.05% success rate with payloads up to 2.3 kg. The approach is further validated through real-world experiments across six representative scenarios with controlled variations in object physical properties (size and mass) and task heights. Specifically, within a wide vertical workspace ranging from ground level to 1.1~m-high tabletops, the system demonstrates an average success rate of 73.3% for payloads up to 1.3 kg, with an average execution time of 4.06 s. Unlike prior works that handle lightweight objects and execute pick-and-place motions with slow, piecewise motions, the proposed framework exploits concurrent locomotion and manipulation for dynamic, continuous execution. These results demonstrate the potential of quadrupedal mobile manipulators for adaptive, whole-body pick-and-place with heavier payloads and extended workspaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15713 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Dynamic Pick-and-Place for a Legged Manipulator Jung, Moonkyu Lee, Jiseong He, Zhengmao Youm, Donghoon Mun, Juhyeok Kim, HyeongJun Oh, Hyunsik Choi, Donghyuk Hur, Jungwoo Song, Jie Hwangbo, Jemin Robotics Artificial Intelligence Legged manipulators extend robotic capabilities beyond static manipulation by integrating agile locomotion with versatile arm control. However, achieving precise manipulation while maintaining coordinated locomotion remains a major challenge. This work presents a hierarchical reinforcement learning framework for dynamic pick-and-place tasks using a quadruped equipped with a 6-DOF robotic arm. The framework incorporates an explicit mass estimation module enabling adaptive whole-body control for objects with varying weights. In simulation, the system achieves an 86.05% success rate with payloads up to 2.3 kg. The approach is further validated through real-world experiments across six representative scenarios with controlled variations in object physical properties (size and mass) and task heights. Specifically, within a wide vertical workspace ranging from ground level to 1.1~m-high tabletops, the system demonstrates an average success rate of 73.3% for payloads up to 1.3 kg, with an average execution time of 4.06 s. Unlike prior works that handle lightweight objects and execute pick-and-place motions with slow, piecewise motions, the proposed framework exploits concurrent locomotion and manipulation for dynamic, continuous execution. These results demonstrate the potential of quadrupedal mobile manipulators for adaptive, whole-body pick-and-place with heavier payloads and extended workspaces. |
| title | Learning Dynamic Pick-and-Place for a Legged Manipulator |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2605.15713 |