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Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control
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Dynamic legged locomotion is a challenging topic because of the lack of established control schemes which can handle aerial phases, short stance times, and high-speed leg swings. In this paper, we propose a controller combining whole-body control (WBC) and model predictive control (MPC). In our framework, MPC finds an optimal reaction force profile over a longer time horizon with a simple model, and WBC computes joint torque, position, and velocity commands based on the reaction forces computed from MPC. Unlike existing WBCs, which attempt to track commanded body trajectories, our controller is focused more on the reaction force command, which allows it to accomplish high speed dynamic locomotion with aerial phases. The newly devised WBC is integrated with MPC and tested on the Mini-Cheetah quadruped robot. To demonstrate the robustness and versatility, the controller is tested on six different gaits in a number of different environments, including outdoors and on a treadmill, reaching a top speed of 3.7 m/s.
Forward citations
Cited by 9 Pith papers
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PAKE: Learning Whole-Body Loco-Manipulation with Partial Kinematic Embeddings
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ADP: Adversarial Dynamics Priors for Physically Grounded Humanoid Locomotion
Replacing adversarial motion-prior features with trajectory-optimization-derived dynamics features improves a humanoid policy's push recovery in simulation (J80 +16.7%, recovery time -47.9% vs AMP).
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KiVi: Kinesthetic-Visuospatial Integration for Dynamic and Safe Egocentric Legged Locomotion
A quadruped locomotion controller that explicitly separates proprioceptive and visual pathways stays stable under camera occlusion and visual corruption that destabilizes fused-vision policies.
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Physically-Feasible Reactive Synthesis for Terrain-Adaptive Locomotion
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A Koopman-operator linear model of a quadruped's single-rigid-body dynamics is used in a linear model predictive controller that tracks speeds and rejects pushes on a Unitree Go1.
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For the Unitree A1 bounding gait, simulation shows duty factor near 0.22 to 0.30 and longer stride durations minimize cost of transport, with hardware tests at 0.5 m/s only partially matching.
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An A*-on-octree planner with a height penalty plans ground-hugging 3D routes for ground vehicles in two simulations using under a tenth of the memory and computation time of uniform-grid A*, at nearly equal path length.
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Bayesian Inverse Physics for Neuro-Symbolic Robot Learning
A position paper arguing that hybrid neuro-symbolic architectures combining physics, Bayesian inference, and program synthesis are essential for general-purpose robot learning.
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