The intricate challenge of articulated tool manipulation robotics has long been a bottleneck in dexterous robotics, primarily due to the complexity of coordinating internal degrees of freedom with contact-rich interactions. Prior research has predominantly tackled rigid objects, leaving the nuanced domain of articulated tools largely unexplored. This gap stems from the inherent physical complexity and the difficulty in learning effective grasping and manipulation policies. The Mana (Manipulation Animator) framework emerges as a general sim-to-real solution, reframing dexterous manipulation as an animation problem.
Animation as a Pipeline for Dexterous Control
Inspired by established computer animation techniques, Mana introduces a coarse-to-fine pipeline. This approach transforms procedurally generated grasp keyframes into sophisticated manipulation trajectories. The core innovation lies in its integration of motion planning and reinforcement learning, enabling the system to navigate the complexities of articulated object interaction. This methodology streamlines the learning process, making it more scalable and efficient for tackling previously intractable problems in articulated tool manipulation robotics.