Wearable Gripper Interface for Intuitive Robot Action Capture
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Solution Overview
Problem
Existing robotic grippers face challenges in executing complex grasping and manipulation tasks in both structured and unstructured environments due to the need for accurate environmental representations and high computational power, limiting their performance and safety in real-world scenarios, and there is a lack of intuitive human-machine interfaces for direct control and data collection of human-driven robot actions.
Innovation Solution
A data collection system comprising a visual sensing subsystem, user interface, human-machine operation interface, wearable computation subsystem, and data store subsystem, which includes forearm and palm-mounted interfaces for operating robotic grippers, enabling real-time data synchronization, processing, and storage 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 is simple, but the ease of operation deteriorates due to lack of intuitive control for complex in-hand manipulation motions
Solution Approach 1:
The patent uses motion capture technology to copy human hand and finger movements, translating them directly to robotic gripper motions. This allows intuitive control where human natural movements are replicated by the robot, solving the ease of operation problem without requiring complex traditional control interfaces.
Solution Approach 2:
The patent replaces traditional mechanical control interfaces (robotic pendants) with a motion capture-based control system. This substitution enables more intuitive control by capturing human motion data and translating it to robotic commands, improving ease of operation while managing device complexity through software-based solutions.
2Measurement precision
If analytical methods and physics based simulation are used for robot grasping and manipulation, then measurement precision is improved, but device complexity worsens due to high computational power requirements and need for accurate environmental representations
Solution Approach 1:
The system uses motion capture data from human operators to directly generate control commands for the robotic gripper, eliminating the need for complex physics-based simulations and environmental modeling. The human operator's movements serve as the direct reference, simplifying the computational requirements while maintaining grasping accuracy.
Solution Approach 2:
The patent collects and stores motion capture data from human operators performing various manipulation tasks in advance. This pre-collected data serves as a library of natural human motions that can be directly applied to robotic control, avoiding the need for real-time complex simulations and reducing computational complexity during actual operation.
3Manufacturing precision
If robotic grippers are mounted on robotic manipulators for experimental validation, then manufacturing precision is improved, but ease of operation deteriorates due to limited reachable workspace
Solution Approach 1:
The patent introduces a portable motion capture-based human-machine interface as an intermediary between the operator and the robotic gripper. This interface allows operators to control the gripper with full workspace accessibility, eliminating the limitations of being constrained to manipulator reachable workspaces while maintaining precise gripper control through accurate motion tracking.
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.