Wearable Gripper Interface for Low-Complexity Robot Learning Data
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Solution Overview
Problem
Current robotic gripper technologies face challenges in executing complex grasping and manipulation tasks in unstructured environments due to the need for accurate environmental representation and high computational complexity, limiting their versatility and safety in real-world applications.
Innovation Solution
A data collection system comprising a visual sensing subsystem, user interface subsystem, human-machine operation interface, wearable computation subsystem, and data store subsystem, which allows human operators to directly control robotic grippers using immersive and intuitive interfaces, collecting data for future robot learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional control interfaces such as robotic pendants are used, then the system structure remains simple, but the ease of operation and control precision for complex dexterous manipulation deteriorates
Solution Approach 1:
The patent replaces traditional mechanical control interfaces (robotic pendants) with a virtual reality-based immersive interface. The VR system uses virtual hands that mirror the user's actual hand movements, allowing direct and intuitive control of robotic gripper motions without complex mechanical linkages or traditional control devices.
Solution Approach 2:
The patent creates virtual copies of the user's hands within the VR environment that replicate hand movements and gestures. These virtual hands serve as intermediaries to control the robotic gripper, copying human hand motions to achieve precise control without requiring the user to learn complex interface operations.
2Manufacturing precision
If analytical and hybrid approaches with highly accurate environmental representation are used, then the manufacturing precision of robot grasping improves, but the computational power and device complexity required deteriorates
Solution Approach 1:
The patent collects comprehensive motion and manipulation data exceeding what is strictly necessary for basic control. This excessive data collection includes detailed kinematic information, force feedback, and environmental interactions, which can be selectively processed to achieve high grasping precision without requiring complete environmental modeling.
Solution Approach 2:
The system pre-collects and stores extensive motion data during human operation phases. This preliminary data collection allows subsequent robot learning and simulation to achieve high precision without requiring complex real-time environmental representation during actual manipulation tasks.
3Productivity
If autonomous robot operation in unstructured environments is implemented, then the productivity increases, but the reliability and safety deteriorates due to health and safety risks
Solution Approach 1:
The patent introduces a portable human-machine interface as an intermediary between the human operator and the autonomous robot system. This interface allows continuous human oversight and intervention during operations in unstructured environments, maintaining safety while enabling autonomous productivity enhancement.
Solution Approach 2:
The system incorporates sensors that capture motion and manipulation data, providing real-time feedback to both the control system and human operators. This feedback loop enables detection of unsafe conditions and allows human intervention when necessary, maintaining reliability while achieving autonomous operation productivity.
Data Source
AI summary
A data collection system that performs data collection of human-driven robot actions for robot learning. The data collection system includes: i) a wearable computation subsystem that is worn by a human data collector and that controls the data collection process and ii) a human-machine operation interface subsystem that allows the human data collector to use the human-machine operation interface to operate an attached robotic gripper to perform one or more actions. A user interface subsystem receives instructions from the wearable computation subsystem that direct the human data collector to perform the one or more actions using the human-machine operation interface subsystem. A visual sensing subsystem includes one or more cameras that collect raw visual data related to the pose and movement of the robotic gripper while performing the one or more actions. A data collection subsystem receives collected data related to the one or more actions.