HCLM presents a hierarchical architecture that uses an SE(3)-invariant diffusion policy for coordination and a hybrid whole-body controller with MPC and admittance control for safe closed-chain loco-manipulation on dual quadrupeds.
Combining learning-based locomotion policy with model-based manipulation for legged mobile manipulators,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.RO 2years
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UNVERDICTED 2representative citing papers
A hierarchical RL framework with an explicit mass estimation module enables dynamic concurrent locomotion and manipulation on a quadruped with arm, achieving 86% success in simulation up to 2.3 kg and 73% in real tests up to 1.3 kg across varied heights and object properties.
citing papers explorer
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HCLM: A Hierarchical Framework for Cooperative Loco-Manipulation with Dual Quadrupeds
HCLM presents a hierarchical architecture that uses an SE(3)-invariant diffusion policy for coordination and a hybrid whole-body controller with MPC and admittance control for safe closed-chain loco-manipulation on dual quadrupeds.
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Learning Dynamic Pick-and-Place for a Legged Manipulator
A hierarchical RL framework with an explicit mass estimation module enables dynamic concurrent locomotion and manipulation on a quadruped with arm, achieving 86% success in simulation up to 2.3 kg and 73% in real tests up to 1.3 kg across varied heights and object properties.