FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation
Journal:
arXiv
Published Date:
May 10, 2025
Abstract
Humanoid loco-manipulation holds transformative potential for daily service
and industrial tasks, yet achieving precise, robust whole-body control with 3D
end-effector force interaction remains a major challenge. Prior approaches are
often limited to lightweight tasks or quadrupedal/wheeled platforms. To
overcome these limitations, we propose FALCON, a dual-agent
reinforcement-learning-based framework for robust force-adaptive humanoid
loco-manipulation. FALCON decomposes whole-body control into two specialized
agents: (1) a lower-body agent ensuring stable locomotion under external force
disturbances, and (2) an upper-body agent precisely tracking end-effector
positions with implicit adaptive force compensation. These two agents are
jointly trained in simulation with a force curriculum that progressively
escalates the magnitude of external force exerted on the end effector while
respecting torque limits. Experiments demonstrate that, compared to the
baselines, FALCON achieves 2x more accurate upper-body joint tracking, while
maintaining robust locomotion under force disturbances and achieving faster
training convergence. Moreover, FALCON enables policy training without
embodiment-specific reward or curriculum tuning. Using the same training setup,
we obtain policies that are deployed across multiple humanoids, enabling
forceful loco-manipulation tasks such as transporting payloads (0-20N force),
cart-pulling (0-100N), and door-opening (0-40N) in the real world.
