Adaptive control improves balance of humanoid robots lifting unknown weights
Adaptive Whole-Body MPC with Optimal Posture Planning for Humanoid Robots under Unknown Payloads
Robotics
Summary
Humanoid robots can struggle to keep their balance when lifting or carrying objects if the weight is unknown or changes unexpectedly. The authors created a system that helps robots adjust in real-time by predicting stable body motions, correcting for weight differences, and planning smooth movements like standing up and sitting down. This combination lets robots handle and move objects more safely and steadily. Their tests with simulations and real robots showed better balance and more accurate movements compared to earlier methods.
What this means in practice
- •For robotics engineers: Design balance control systems that maintain humanoid robot stability when lifting unknown or varying payloads in real environments.
- •For robot system integrators: Implement adaptive whole-body controllers to improve performance of robots performing material handling and lifting tasks in warehouses or factories.
Authors
Manh Cuong Nguyen, Duc Cuong Vu, Viet Anh Vu, Duc Tuan Pham, Toan Nguyen, Minh Hiep Phung, Ngo Anh Vien, Minh Nhat Vu
Abstract
Humanoid robots performing lifting and material-handling tasks must maintain balance despite unknown and varying payloads. However, payload uncertainty significantly affects whole-body dynamics and may destabilize conventional controllers. We propose an adaptive whole-body control architecture that integrates three components: (i) a centroidal model predictive controller (MPC) that generates dynamically feasible reference motions, (ii) an adaptive fuzzy balance controller that compensates for payload-induced model mismatch by generating corrective torques distributed across the supporting-leg joints, and (iii) a waypoint-based optimal posture planner built on a triple-integrator model that produces smooth sit-to-stand and stand-to-sit trajectories satisfying center-of-mass (CoM) stability constraints, joint limits, and whole-body feasibility requirements. Together, these components enable stable lowering, grasping, lifting, carrying, and placing behaviors under significant payload uncertainty. Extensive simulation and hardware experiments demonstrate that the proposed framework improves balance robustness and reduces tracking degradation during posture transitions and lifting tasks compared to non-adaptive baselines.