Functional Eigen-Grasping Using Approach Heatmaps
CoRR(2024)
摘要
This work presents a framework for a robot with a multi-fingered hand to
freely utilize daily tools, including functional parts like buttons and
triggers. An approach heatmap is generated by selecting a functional finger,
indicating optimal palm positions on the object's surface that enable the
functional finger to contact the tool's functional part. Once the palm position
is identified through the heatmap, achieving the functional grasp becomes a
straightforward process where the fingers stably grasp the object with
low-dimensional inputs using the eigengrasp. As our approach does not need
human demonstrations, it can easily adapt to various sizes and designs,
extending its applicability to different objects. In our approach, we use
directional manipulability to obtain the approach heatmap. In addition, we add
two kinds of energy functions, i.e., palm energy and functional energy
functions, to realize the eigengrasp. Using this method, each robotic gripper
can autonomously identify its optimal workspace for functional grasping,
extending its applicability to non-anthropomorphic robotic hands. We show that
several daily tools like spray, drill, and remotes can be efficiently used by
not only an anthropomorphic Shadow hand but also a non-anthropomorphic Barrett
hand.
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