title: Soft Robotic Dynamic In-Hand Pen Spinning
summary: Dynamic in-hand manipulation remains a challenging task for soft robotic
  systems that have demonstrated advantages in safe compliant interactions but
  struggle with high-speed dynamic tasks. In this work, we present SWIFT, a
  system for learning dynamic tasks using a soft and compliant robotic hand.
  Unlike previous works that rely on simulation, quasi-static actions and precise
  object models, the proposed system learns to spin a pen through trial-and-error
  using only real-world data without requiring explicit prior knowledge of the
  pen's physical attributes. With self-labeled trials sampled from the real
  world, the system discovers the set of pen grasping and spinning primitive
  parameters that enables a soft hand to spin a pen robustly and reliably. After
  130 sampled actions per object, SWIFT achieves 100% success rate across three
  pens with different weights and weight distributions, demonstrating the
  system's generalizability and robustness to changes in object properties. The
  results highlight the potential for soft robotic end-effectors to perform
  dynamic tasks including rapid in-hand manipulation. We also demonstrate that
  SWIFT generalizes to spinning items with different shapes and weights such as a
  brush and a screwdriver which we spin with 10/10 and 5/10 success rates
  respectively. Videos, data, and code are available at
  https://soft-spin.github.io.
paper_authors._id: 673d69231b0fe168ad4ba290
paper_authors.name: Yunchao Yao
paper_authors._id: 673d69231b0fe168ad4ba291
paper_authors.name: Uksang Yoo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-20T13:27:27.098Z
paper_authors._id: 666ae4e53315aa1fa833bbcd
paper_authors.avatarUrl: /avatars/a8c541c7408a4438fe4409b746e4f2d6.svg
paper_authors.fullname: Uksang Yoo
paper_authors.type: user
paper_authors.user: uyoo344
paper_authors._id: 673d69231b0fe168ad4ba292
paper_authors.name: Jean Oh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-20T12:34:24.106Z
paper_authors._id: 638779412ee2261893db9872
paper_authors.avatarUrl: /avatars/98e1d86afbb8a105db946703fa44d430.svg
paper_authors.fullname: Jean Oh
paper_authors.type: user
paper_authors.user: jeanoh
paper_authors._id: 673d69231b0fe168ad4ba293
paper_authors.name: Christopher G. Atkeson
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-20T12:34:33.830Z
paper_authors._id: 667207df662051878805403f
paper_authors.avatarUrl: /avatars/53130f7d5eba540ef87085ebfeeb8d7a.svg
paper_authors.fullname: Christopher Atkeson
paper_authors.type: user
paper_authors.user: cgacgacga
paper_authors._id: 673d69231b0fe168ad4ba294
paper_authors.name: Jeffrey Ichnowski
paper_title: Soft Robotic Dynamic In-Hand Pen Spinning
paper_summary: Dynamic in-hand manipulation remains a challenging task for soft robotic
  systems that have demonstrated advantages in safe compliant interactions but
  struggle with high-speed dynamic tasks. In this work, we present SWIFT, a
  system for learning dynamic tasks using a soft and compliant robotic hand.
  Unlike previous works that rely on simulation, quasi-static actions and precise
  object models, the proposed system learns to spin a pen through trial-and-error
  using only real-world data without requiring explicit prior knowledge of the
  pen's physical attributes. With self-labeled trials sampled from the real
  world, the system discovers the set of pen grasping and spinning primitive
  parameters that enables a soft hand to spin a pen robustly and reliably. After
  130 sampled actions per object, SWIFT achieves 100% success rate across three
  pens with different weights and weight distributions, demonstrating the
  system's generalizability and robustness to changes in object properties. The
  results highlight the potential for soft robotic end-effectors to perform
  dynamic tasks including rapid in-hand manipulation. We also demonstrate that
  SWIFT generalizes to spinning items with different shapes and weights such as a
  brush and a screwdriver which we spin with 10/10 and 5/10 success rates
  respectively. Videos, data, and code are available at
  https://soft-spin.github.io.
paper_ai_keywords: soft robotic systems dynamic tasks soft and compliant robotic hand trial-and-error real-world data self-labeled data pen grasping adaptive parameters dynamic manipulation rapid in-hand manipulation generalizability robustness different object properties different shapes