Learning Dynamic Pick-and-Place for a Legged Manipulator

• URL: http://arxiv.org/abs/2605.15713v1
• Date: Fri, 15 May 2026 08:04:07 GMT
• Title: Learning Dynamic Pick-and-Place for a Legged Manipulator
• Abstract summary: This work presents a hierarchical reinforcement learning framework for dynamic pick-and-place tasks using a quadruped equipped with a 6-DOF robotic arm.<n>In simulation, the system achieves an 86.05% success rate with payloads up to 2.3 kg.<n>Within a wide vertical workspace ranging from ground level to 1.1m-high tabletops, the system demonstrates an average success rate of 73.3% for payloads up to 1.3 kg.
• Score: 8.027246382283003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. 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.

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