Soft Robotic Dynamic In-Hand Pen Spinning
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.
673d69231b0fe168ad4ba290
Yunchao Yao
673d69231b0fe168ad4ba291
Uksang Yoo
claimed_verified
2024-11-20T13:27:27.098Z
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Uksang Yoo
user
uyoo344
673d69231b0fe168ad4ba292
Jean Oh
admin_assigned
2024-11-20T12:34:24.106Z
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/avatars/98e1d86afbb8a105db946703fa44d430.svg
Jean Oh
user
jeanoh
673d69231b0fe168ad4ba293
Christopher G. Atkeson
admin_assigned
2024-11-20T12:34:33.830Z
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/avatars/53130f7d5eba540ef87085ebfeeb8d7a.svg
Christopher Atkeson
user
cgacgacga
673d69231b0fe168ad4ba294
Jeffrey Ichnowski
Soft Robotic Dynamic In-Hand Pen Spinning
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.
SWIFT, a system for learning dynamic pen spinning tasks with a soft robotic hand, achieves high success rates using real-world data without prior object knowledge, highlighting the potential for soft robotics in dynamic manipulation.
soft robotic systems dynamic in-hand manipulation soft and compliant robotic hand trial-and-error learning self-labeled trials pen grasping spinning primitive parameters soft hand dynamic tasks rapid in-hand manipulation