Robot Gripper Tactile Control for In-Grasp Sliding Velocity
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Solution Overview
Problem
Current robotic systems face challenges in controlling in-grasp sliding velocities of objects, which is crucial for gentle placement in cluttered environments, as they lack effective methods to estimate and manipulate object velocities based on tactile sensing data and visual feedback.
Innovation Solution
A neural network-based tool control system that processes tactile sensing data, including impedance, static pressure, and dynamic pressure, to estimate object velocity and generate control parameters for a robot gripper or end effector, allowing for precise control of object sliding by adjusting finger distance, integrated with visual feedback for object property estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If direct placement via pick-and-place methods is used, then placement accuracy is improved, but the method becomes infeasible in cluttered environments with obstructing items
Solution Approach 1:
The system transitions from static pick-and-place operations to dynamic in-grasp sliding manipulation. The robot arm continuously adjusts the object's position and velocity while maintaining grasp, enabling adaptation to cluttered environments where direct placement is blocked by obstacles
Solution Approach 2:
The system employs tactile sensors mounted on the robot gripper to provide real-time feedback on contact forces and slip detection during in-grasp sliding. This feedback loop enables continuous adjustment of manipulation forces and velocity to maintain controlled sliding despite environmental obstacles
2Adaptability or versatility
If in-grasp sliding is used to achieve gentle placement in cluttered environments, then adaptability is improved, but control precision of sliding velocity becomes challenging
Solution Approach 1:
Tactile sensors on the gripper provide real-time feedback on contact forces and slip detection during in-grasp sliding. This feedback loop enables continuous adjustment of manipulation forces and velocity to maintain controlled sliding despite environmental obstacles
Solution Approach 2:
The system replaces traditional mechanical velocity control with a neural network-based estimation and control system. The neural network processes tactile sensor data to estimate sliding velocity and generates appropriate control commands, achieving precise velocity control through intelligent algorithms rather than purely mechanical means
3Measurement precision
If tactile sensors are added to the robot gripper for velocity estimation, then measurement capability is improved, but device complexity increases
Solution Approach 1:
The tactile sensor array on the gripper serves multiple functions: contact detection, slip detection, force measurement, and velocity estimation. This multi-functionality justifies the added complexity by providing comprehensive manipulation feedback from a single sensor integration
Solution Approach 2:
The system replaces complex mechanical velocity sensing mechanisms with a neural network that processes simpler tactile sensor data. The neural network estimates sliding velocity by analyzing patterns in tactile feedback, achieving velocity measurement capability through software intelligence rather than complex mechanical sensors
Data Source
AI summary
A tool control system may include: a tactile sensor configured to, when a tool holds a target object and slides the target object downward across the tool, obtain tactile sensing data from the tool; one or more memories configured to store a target velocity and computer-readable instructions; and one or more processors configured execute the computer-readable instructions to: receive the tactile sensing data from the tactile sensor; estimate a velocity of the target object based on the tactile sensing data, by using one or more neural networks that are trained based on a training image of an sample object captured while the sample object is sliding down; and generate a control parameter of the tool based on the estimated velocity and the target velocity.