SigLoMa enables dynamic loco-manipulation on quadrupeds from ego-centric 5 Hz vision alone by using Sigma Points for scalable exteroception, an ego-centric Kalman Filter for high-rate state estimation, and an active sampling curriculum, matching expert human teleoperation performance.
Whole- body end-effector pose tracking,
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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.
Reinforcement learning with a constellation-based reward enables direct, efficient humanoid locomotion to short-range SE(2) targets, outperforming velocity-tracking baselines in simulation and transferring to hardware.
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SigLoMa: Learning Open-World Quadrupedal Loco-Manipulation from Ego-Centric Vision
SigLoMa enables dynamic loco-manipulation on quadrupeds from ego-centric 5 Hz vision alone by using Sigma Points for scalable exteroception, an ego-centric Kalman Filter for high-rate state estimation, and an active sampling curriculum, matching expert human teleoperation performance.
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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.
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No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets
Reinforcement learning with a constellation-based reward enables direct, efficient humanoid locomotion to short-range SE(2) targets, outperforming velocity-tracking baselines in simulation and transferring to hardware.