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LocoMan: Advancing Versatile Quadrupedal Dexterity with Lightweight Loco-Manipulators
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Quadrupedal robots have emerged as versatile agents capable of locomoting and manipulating in complex environments. Traditional designs typically rely on the robot's inherent body parts or incorporate top-mounted arms for manipulation tasks. However, these configurations may limit the robot's operational dexterity, efficiency and adaptability, particularly in cluttered or constrained spaces. In this work, we present LocoMan, a dexterous quadrupedal robot with a novel morphology to perform versatile manipulation in diverse constrained environments. By equipping a Unitree Go1 robot with two low-cost and lightweight modular 3-DoF loco-manipulators on its front calves, LocoMan leverages the combined mobility and functionality of the legs and grippers for complex manipulation tasks that require precise 6D positioning of the end effector in a wide workspace. To harness the loco-manipulation capabilities of LocoMan, we introduce a unified control framework that extends the whole-body controller (WBC) to integrate the dynamics of loco-manipulators. Through experiments, we validate that the proposed whole-body controller can accurately and stably follow desired 6D trajectories of the end effector and torso, which, when combined with the large workspace from our design, facilitates a diverse set of challenging dexterous loco-manipulation tasks in confined spaces, such as opening doors, plugging into sockets, picking objects in narrow and low-lying spaces, and bimanual manipulation.
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Cited by 4 Pith papers
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Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining
Pretraining a modular transformer policy on human demonstrations then finetuning on a small robot dataset improves success on six real quadruped manipulation tasks, including out-of-distribution objects.
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SLIM: Sim-to-Real Legged Instructive Manipulation via Long-Horizon Visuomotor Learning
A single policy trained purely in simulation with a hierarchical teacher-student pipeline solves long-horizon search-grasp-transport-drop tasks on a low-cost quadruped with about 78% real-world success.
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Bipedalism for Quadrupedal Robots: Versatile Loco-Manipulation through Risk-Adaptive Reinforcement Learning
The paper trains a quadrupedal robot to walk bipedally using a risk-adaptive distributional reinforcement learning method, and demonstrates front-leg manipulation in simulation and on a Unitree Go2.
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Mobile-TeleVision: Predictive Motion Priors for Humanoid Whole-Body Control
A decoupled humanoid controller combines IK-based arm control with an RL locomotion policy conditioned on a CVAE motion prior, improving manipulation precision while maintaining walking stability.
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