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Learning Visual Quadrupedal Loco-Manipulation from Demonstrations
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Quadruped robots are progressively being integrated into human environments. Despite the growing locomotion capabilities of quadrupedal robots, their interaction with objects in realistic scenes is still limited. While additional robotic arms on quadrupedal robots enable manipulating objects, they are sometimes redundant given that a quadruped robot is essentially a mobile unit equipped with four limbs, each possessing 3 degrees of freedom (DoFs). Hence, we aim to empower a quadruped robot to execute real-world manipulation tasks using only its legs. We decompose the loco-manipulation process into a low-level reinforcement learning (RL)-based controller and a high-level Behavior Cloning (BC)-based planner. By parameterizing the manipulation trajectory, we synchronize the efforts of the upper and lower layers, thereby leveraging the advantages of both RL and BC. Our approach is validated through simulations and real-world experiments, demonstrating the robot's ability to perform tasks that demand mobility and high precision, such as lifting a basket from the ground while moving, closing a dishwasher, pressing a button, and pushing a door. Project website: https://zhengmaohe.github.io/leg-manip
Forward citations
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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Unsupervised Skill Discovery as Exploration for Learning Agile Locomotion
A robot training framework, SDAX, uses unsupervised skill discovery as an exploration signal with a learned balancing weight, letting a Unitree A1 learn leap, climb, crawl, and wall-jump behaviors in simulation and on...
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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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QUART-Online: Latency-Free Large Multimodal Language Model for Quadruped Robot Learning
Compressing 10-step action chunks into discrete latent codes lets an 8B multimodal model drive a quadruped at controller frequency and raises average task success by about 65%.
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