Robots are increasingly envisioned as human companions, assisting with
everyday tasks that often involve manipulating deformable objects. Although
recent advances in robotic hardware and embodied AI have expanded their
capabilities, current systems still struggle with handling thin, flat, E
deformable objects such as paper and fabric. This limitation arises from the
lack of suitable perception techniques for robust state estimation under
diverse object appearances, as well as the absence of planning techniques for
generating appropriate grasp motions. To bridge these gaps, this paper
introduces PP-Tac, a robotic system for picking up paper-like objects. PP-Tac
features a multi-fingered robotic hand with high-resolution omnidirectional
tactile sensors \sensorname. This hardware configuration enables real-time slip
detection and online frictional force control that mitigates such slips.
Inoltre, grasp motion generation is achieved through a trajectory synthesis
pipeline, which first constructs a dataset of finger’s pinching motions. Based
on this dataset, a diffusion-based policy is trained to control the hand-arm
robotic system. Experiments demonstrate that PP-Tac can effectively grasp
paper-like objects of varying material, thickness, and stiffness, achieving an
overall success rate of 87.5\%. To our knowledge, this work is the first
attempt to grasp paper-like deformable objects using a tactile dexterous hand.
Our project webpage can be found at:
https://peilin-666.github.io/projects/PP-Tac/

Questo articolo esplora i giri e le loro implicazioni.

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