SkeleWAM: Skeleton World-Action Modeling for Efficient Robotic Manipulation
SkeleWAM combines robot action generation and future state prediction using a sparse 3D skeleton representation of manipulation scenes. It models the scene as robot joints and object landmarks, reducing complexity compared to visual methods. The model learns to predict actions and future skeleton states jointly, enabling efficient robotic action learning.