Learning Dynamic Pick-and-Place for a Legged Manipulator
A new study presents a hierarchical reinforcement learning framework for dynamic pick-and-place tasks using a quadruped robot with a 6-DOF arm. The system achieves an 86.05% success rate in simulations and a 73.3% success rate in real-world scenarios with varying payloads. This research highlights the potential of legged manipulators for adaptive and efficient robotic tasks.
- ▪Legged manipulators combine agile locomotion with versatile arm control for improved robotic capabilities.
- ▪The proposed framework includes a mass estimation module for adaptive whole-body control of objects with different weights.
- ▪In real-world tests, the system demonstrated a 73.3% success rate for payloads up to 1.3 kg.
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Computer Science > Robotics arXiv:2605.15713 (cs) [Submitted on 15 May 2026] Title:Learning Dynamic Pick-and-Place for a Legged Manipulator Authors:Moonkyu Jung, Jiseong Lee, Zhengmao He, Donghoon Youm, Juhyeok Mun, HyeongJun Kim, Hyunsik Oh, Donghyuk Choi, Jungwoo Hur, Jie Song, Jemin Hwangbo View a PDF of the paper titled Learning Dynamic Pick-and-Place for a Legged Manipulator, by Moonkyu Jung and 10 other authors View PDF HTML (experimental) Abstract: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.
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