Deep Learning Tactile Force Estimation for Accurate Robot Handling
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Solution Overview
Problem
Existing robotic systems face challenges in accurately estimating force magnitude and direction using tactile sensors, which is crucial for performing complex tasks like handling delicate objects or manipulating soft materials, due to the difficulty in accurately interpreting the force exerted by tactile sensors.
Innovation Solution
A system that calibrates tactile force sensors using a neural network trained with data from various test fixtures, allowing for accurate estimation of force magnitude and direction by encoding spatial information from the sensor electrodes and regularizing the loss function to improve accuracy, particularly in tasks involving flexible and compliant objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tactile sensors are used to sense force, then the robot can perform subtle tasks, but the force estimation accuracy is insufficient
Solution Approach 1:
The patent replaces traditional mechanical force sensing methods with a neural network-based deep learning system. The neural network learns to estimate contact force and torque from tactile sensor signals, substituting complex mechanical interpretation with trained computational models that achieve higher accuracy in force estimation.
Solution Approach 2:
The patent introduces a neural network as an intermediary between the tactile sensor and the force estimation process. This intermediary layer processes the raw sensor signals and translates them into accurate force and torque estimates, bridging the gap between sensor output and meaningful physical quantities.
2Reliability
If sophisticated tactile sensors are used, then delicate objects can be handled, but accurate force estimation remains difficult
Solution Approach 1:
The patent applies preliminary action by training the neural network offline with extensive simulation data before deployment. The network is pre-trained to recognize patterns between tactile sensor signals and ground truth force/torque values, so that during actual object handling, the force estimation can be performed accurately without real-time training or complex measurements.
3Measurement precision
If traditional force sensing methods are used, then the system is simpler, but the force estimation accuracy is insufficient for complex tasks
Solution Approach 1:
The patent replaces traditional mechanical force sensing and calculation methods with a neural network-based system. Instead of using complex mechanical models to interpret sensor data, the system uses a trained neural network that directly maps tactile sensor signals to accurate force magnitude and direction estimates.
Solution Approach 2:
The patent changes the parameters of the sensing system by transitioning from direct mechanical measurement to learned parameter estimation. The neural network learns optimal parameter mappings from simulation data, enabling accurate force estimation even with simplified or indirect tactile sensors.
Data Source
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AI summary
A computer system generates a tactile force model for a tactile force sensor by performing a number of calibration tasks. In various embodiments, the calibration tasks include pressing the tactile force sensor while the tactile force sensor is attached to a pressure gauge, interacting with a ball, and pushing an object along a planar surface. Data collected from these calibration tasks is used to train a neural network. The resulting tactile force model allows the computer system to convert signals received from the tactile force sensor into a force magnitude and direction with greater accuracy than conventional methods. In an embodiment, force on the tactile force sensor is inferred by interacting with an object, determining the motion of the object, and estimating the forces on the object based on a physical model of the object.