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Talent Scout
J
Junjie Fang
Shanghai Jiao Tong University
W
Wendi Chen
Shanghai Innovation Institute
H
Han Xue
Shanghai Jiao Tong University
F
Fangyuan Zhou
Shanghai Innovation Institute
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Robotics experts on LinkedIn & GitHub
References (47)
Founder's Pitch
"RoboPocket enables rapid robot policy improvements using consumer smartphones for real-time feedback and data collection."
Commercial Viability Breakdown
0-10 scaleHigh Potential
3/4 signals
Quick Build
4/4 signals
Series A Potential
2/4 signals
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Why It Matters
This research matters because it democratizes robot policy improvement by leveraging consumer hardware, thus significantly lowering the barrier for entry in robotic learning and development, which traditionally requires specialized and costly equipment.
Product Angle
The product can be offered as a SaaS platform where users leverage their smartphones and AR to fine-tune robot policies. It integrates with the robots' existing infrastructure to provide rapid policy updates and performance improvements.
Disruption
It has the potential to replace traditional robotic testing setups that require physical robots and controlled environments with a more accessible and cost-effective smartphone-based solution.
Product Opportunity
The market size includes robotics research labs, developers, and companies aiming for rapid prototyping and iteration. The primary pain point addressed is the high cost and logistical challenges of using physical robots for real-time policy testing, with developers or robotics teams paying for access.
Use Case Idea
An app for robotics researchers and developers to rapidly test and iterate on robot control policies using their smartphones, reducing the costs and logistics involved in traditional robotic testing setups.
Science
RoboPocket combines AR visual foresight with asynchronous online finetuning to enable real-time feedback and instant updates to robot policies using only a smartphone. Users can visualize proposed trajectories in AR, identify weaknesses, and provide corrected data for policy updates, bridging the gap between data collection and real-time policy iteration without the need of a physical robot.
Method & Eval
RoboPocket was tested across various real-world tasks such as block sorting and towel folding. The method showed a doubling in data efficiency compared to offline methods and allowed for scalable policy adaptation, achieving significant improvements in distributed environments.
Caveats
Results may be limited by smartphone hardware capabilities, such as sensor precision and computational power. There may also be challenges in capturing the full complexity of physical interactions without the use of actual robots.
Author Intelligence
Junjie Fang
Shanghai Jiao Tong University
Wendi Chen
Shanghai Innovation Institute
Han Xue
Shanghai Jiao Tong University
Fangyuan Zhou
Shanghai Innovation Institute
Tian Le
Shanghai Jiao Tong University
Yi Wang
Shanghai Innovation Institute
Yuting Zhang
Noematrix Ltd.
Jun Lv
Noematrix Ltd.
Chuan Wen
Shanghai Jiao Tong University
Cewu Lu
Shanghai Jiao Tong University