Adaptive Whole-Body MPC with Optimal Posture Planning for Humanoid Robots under Unknown Payloads

Key points

• However, payload uncertainty significantly affects whole-body dynamics and may destabilize conventional controllers.1
• Together, these components enable stable lowering, grasping, lifting, carrying, and placing behaviors under significant payload uncertainty.1
• 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.1

Sources (1)

• [1]Adaptive Whole-Body MPC with Optimal Posture Planning for Humanoid Robots under Unknown Payloads
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 04:12 AM

  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.

Extractive summary: sentences quoted from the sources.