Robotics & AI
RoboPocket: Improve Robot Policies Instantly with Your Phone
RoboPocket introduces a novel approach to robot learning, enabling instant policy updates in distributed environments using consumer smartphones. By leveraging AR Visual Foresight, users can proactively identify and correct policy weaknesses without needing a physical robot, significantly improving data collection efficiency and scalability compared to traditional methods.
Executive Impact
RoboPocket redefines robot learning by making it faster, more accessible, and scalable, directly impacting key operational metrics.
Data Efficiency Boost
Instant Policy Feedback Loop
Gripper BOM Cost
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
RoboPocket enables policy updates in minutes without a physical robot. Users visualize the policy's predicted trajectory via Augmented Reality (AR) Visual Foresight on a smartphone to proactively identify and correct policy distribution shifts. This breaks the traditional feedback loop bottleneck.
Unlike passive recording, RoboPocket integrates real-time visual feedback like SLAM stability and kinematic feasibility into the handheld interface. This active guidance empowers users to collect higher-quality data and self-correct during demonstrations, significantly reducing unusable data.
The system features a low-cost, 3D-printed adaptive gripper that is kinematically and visually isomorphic to industrial grippers like the Robotiq 2F-85. This design ensures physical consistency, allowing collected data to be directly transferable to the target hardware without complex domain adaptation.
2X
Faster Policy Adaptation
RoboPocket Policy Iteration Workflow
Policy Updating In Minutes
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Follow Policy's Intent (AR Visual Foresight)
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Collect Correction and Upload
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Instant Policy Update
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Real-World Application: Distributed Block Sorting
In distributed experiments, RoboPocket demonstrated robust generalization. Multiple users in different environments collectively improved the base policy's success rate by up to 2x with as few as 12 interactive corrections per user, showcasing effective finetuning in the wild.
Estimate Your AI-Driven Efficiency Gains
Quantify the potential impact of RoboPocket's instant policy iteration on your enterprise operations. Adjust the parameters below to see your projected annual savings and reclaimed human hours.
Annual Savings
$0
Hours Reclaimed Annually
0
Your Enterprise AI Roadmap
Our phased approach ensures a smooth, effective, and impactful integration of RoboPocket into your existing operations.
Phase 1: Discovery & Strategy
Initial consultation to assess current workflows, identify key automation opportunities, and define project scope.
Phase 2: Data & Model Training
Collection of target-specific data using RoboPocket, initial policy training, and baseline performance establishment.
Phase 3: Iterative Refinement & Deployment
Continuous policy iteration with AR Visual Foresight, targeted data collection, and integration into existing systems.
Ready to Transform Your Robotics Workflow?
Connect with our AI specialists to explore how RoboPocket can drive instant policy improvement and scalability in your operations.
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