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Founder's Pitch
"A perceptive shared autonomy system that adapts robotic manipulation based on deep learning uncertainty for industrial use."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
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4/4 signals
Series A Potential
3/4 signals
Sources used for this analysis
arXiv Paper
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Why It Matters
The research addresses the critical issue of safety and reliability in robotic systems using deep learning by providing a mechanism to handle the inherent uncertainties in DL perceptions, particularly in high-stakes environments like industrial settings.
Product Angle
Develop a robotic manipulation system that enhances existing industrial robots by integrating uncertainty-aware shared autonomy, significantly improving safety and flexibility in uncertain environments.
Disruption
Could replace manual inspection and existing robotic systems that lack the ability to dynamically adjust autonomy based on perception uncertainties.
Product Opportunity
Industrial sectors, especially oil and gas, where robotic inspection and maintenance are critical. Customers include companies looking to improve safety and efficiency in hazardous environments while minimizing human intervention.
Use Case Idea
Automating industrial inspection tasks in hazardous areas using robotic manipulators that can transition control levels based on perception uncertainty, thereby reducing human risk and improving operational efficiency.
Science
The study introduces 'perceptive shared autonomy', incorporating uncertainty estimates from DL-based perception to modulate robotic manipulation autonomy levels. It uses Neural Tangent Kernels (NTK) for uncertainty-aware point cloud registration and adjusts control between semi-autonomous manipulation and haptic teleoperation based on perception confidence.
Method & Eval
Demonstrated on aerial manipulation tasks with a user study involving 15 participants and real-world scenarios, showing enhanced reliability and performance of robotic manipulation despite failures in DL-based perception.
Caveats
Relies on accurate uncertainty estimates which, if incorrect, could lead to inappropriate autonomy levels. Additionally, system's demonstration in controlled settings may not fully reflect industrial operational conditions.
Author Intelligence
Jongseok Lee
DLR
jongseok.lee@dlr.de
Ribin Balachandran
DLR
Harsimran Singh
DLR
Jianxiang Feng
DLR
Hrishik Mishra
DLR
Marco De Stefano
DLR
Rudolph Triebel
DLR, KIT
Alin Albu-Schaeffer
DLR, TU Munich
Konstantin Kondak
DLR