463 research outputs found
Design of a Multimodal Fingertip Sensor for Dynamic Manipulation
We introduce a spherical fingertip sensor for dynamic manipulation. It is
based on barometric pressure and time-of-flight proximity sensors and is
low-latency, compact, and physically robust. The sensor uses a trained neural
network to estimate the contact location and three-axis contact forces based on
data from the pressure sensors, which are embedded within the sensor's sphere
of polyurethane rubber. The time-of-flight sensors face in three different
outward directions, and an integrated microcontroller samples each of the
individual sensors at up to 200 Hz. To quantify the effect of system latency on
dynamic manipulation performance, we develop and analyze a metric called the
collision impulse ratio and characterize the end-to-end latency of our new
sensor. We also present experimental demonstrations with the sensor, including
measuring contact transitions, performing coarse mapping, maintaining a contact
force with a moving object, and reacting to avoid collisions.Comment: 6 pages, 2 pages of references, supplementary video at
https://youtu.be/HGSdcW_aans. Submitted to ICRA 202
Proprioceptive Learning with Soft Polyhedral Networks
Proprioception is the "sixth sense" that detects limb postures with motor
neurons. It requires a natural integration between the musculoskeletal systems
and sensory receptors, which is challenging among modern robots that aim for
lightweight, adaptive, and sensitive designs at a low cost. Here, we present
the Soft Polyhedral Network with an embedded vision for physical interactions,
capable of adaptive kinesthesia and viscoelastic proprioception by learning
kinetic features. This design enables passive adaptations to omni-directional
interactions, visually captured by a miniature high-speed motion tracking
system embedded inside for proprioceptive learning. The results show that the
soft network can infer real-time 6D forces and torques with accuracies of
0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also
incorporate viscoelasticity in proprioception during static adaptation by
adding a creep and relaxation modifier to refine the predicted results. The
proposed soft network combines simplicity in design, omni-adaptation, and
proprioceptive sensing with high accuracy, making it a versatile solution for
robotics at a low cost with more than 1 million use cycles for tasks such as
sensitive and competitive grasping, and touch-based geometry reconstruction.
This study offers new insights into vision-based proprioception for soft robots
in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International
Journal of Robotics Research for revie
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