Online iLQR-MPC with Whole-Body Control for Robust Humanoid Locomotion
Overview
This research paper introduces an online iterative linear-quadratic-regulator model predictive control (iLQR-MPC) framework integrated with whole-body control to achieve robust humanoid locomotion.
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Event evidence and corrections
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Environment: simulation
“ving contact feasibility. The model-based formulation makes disturbance responses interpretable through explicit costs and constraints, and requires no platform-specific policy training. In MuJoCo simulations, the iLQR-MPC replans at 100 Hz (mean solve time 8.9 ms); the Unitree G1 sustains steady-state walking and recovers from impulsive 245 N forward and 175 N lateral pushes.”
Exact source · revision 1Source owner not reported
Reported quantity · solve time: 8.9 other · Basis not reported
“ving contact feasibility. The model-based formulation makes disturbance responses interpretable through explicit costs and constraints, and requires no platform-specific policy training. In MuJoCo simulations, the iLQR-MPC replans at 100 Hz (mean solve time 8.9 ms); the Unitree G1 sustains steady-state walking and recovers from impulsive 245 N forward and 175 N lateral pushes.”
Exact source · revision 1Source owner not reported
Reported quantity · replan frequency: 100 other · Basis not reported
“ving contact feasibility. The model-based formulation makes disturbance responses interpretable through explicit costs and constraints, and requires no platform-specific policy training. In MuJoCo simulations, the iLQR-MPC replans at 100 Hz (mean solve time 8.9 ms); the Unitree G1 sustains steady-state walking and recovers from impulsive 245 N forward and 175 N lateral pushes.”
Exact source · revision 1Source owner not reported
Reported quantity · control frequency: 500 other · Basis not reported
“iterative linear-quadratic-regulator MPC (iLQR-MPC) then jointly optimizes contact forces and footstep locations in a single receding-horizon problem, without a separate step planner. Specifically, contact and motion constraints are incorporated into the backward pass through augmented-Lagrangian and relaxed-barrier penalties. A whole-body controller subsequently maps the plan to joint torques at 500 Hz while preser”
Exact source · revision 1Source owner not reported
Reported quantity · push force (forward): 245 other · Basis not reported
“ving contact feasibility. The model-based formulation makes disturbance responses interpretable through explicit costs and constraints, and requires no platform-specific policy training. In MuJoCo simulations, the iLQR-MPC replans at 100 Hz (mean solve time 8.9 ms); the Unitree G1 sustains steady-state walking and recovers from impulsive 245 N forward and 175 N lateral pushes.”
Exact source · revision 1Source owner not reported
Reported quantity · push force (lateral): 175 other · Basis not reported
“ving contact feasibility. The model-based formulation makes disturbance responses interpretable through explicit costs and constraints, and requires no platform-specific policy training. In MuJoCo simulations, the iLQR-MPC replans at 100 Hz (mean solve time 8.9 ms); the Unitree G1 sustains steady-state walking and recovers from impulsive 245 N forward and 175 N lateral pushes.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalOnline iLQR-MPC with Whole-Body Control for Robust Humanoid Locomotion
This research paper introduces an online iterative linear-quadratic-regulator model predictive control (iLQR-MPC) framework integrated with whole-body control to achieve robust humanoid locomotion.
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