force estimation
gelsight-force-estimation
Dataset Details
This dataset contains paired tactile and force data, intended for use in predicting 3-axis normal and shear forces applied to the sensor's elastomer. We used three different indenter shapes to collect force-labeled data: hemisphere, sharp, and flat. To measure force ground truths, we employed the ATI nano17 force/torque sensor. The protocol consisted of applying a random normal load (up to 3N) followed by a shear load, achieved by sliding the probe 2mm on the… See the full description on the dataset page: https://huggingface.co/datasets/facebook/gelsight-force-estimation.digit-force-estimation
Dataset Details
This dataset contains paired tactile and force data, intended for use in predicting 3-axis normal and shear forces applied to the sensor's elastomer. We used three different indenter shapes to collect force-labeled data: hemisphere, sharp, and flat. To measure force ground truths, we employed the ATI nano17 force/torque sensor. The protocol consisted of applying a random normal load (up to 5N) followed by a shear load, achieved by sliding the probe 2mm on the… See the full description on the dataset page: https://huggingface.co/datasets/facebook/digit-force-estimation.digit-force-estimation
Dataset Details
This dataset contains paired tactile and force data, intended for use in predicting 3-axis normal and shear forces applied to the sensor's elastomer. We used three different indenter shapes to collect force-labeled data: hemisphere, sharp, and flat. To measure force ground truths, we employed the ATI nano17 force/torque sensor. The protocol consisted of applying a random normal load (up to 5N) followed by a shear load, achieved by sliding the probe 2mm on the… See the full description on the dataset page: https://huggingface.co/datasets/Bbbbbhyyyy/digit-force-estimation.paper_latella_2023_irim_muscle-force-estimation_dataset
Real-time Lower Leg Muscle Forces Estimation using a Hill-type Model and Whole-body Wearable Sensors
Claudia Latella, Antonella Tatarelli, Lorenzo Fiori, Riccardo Grieco, Lorenzo Rapetti, Daniele Pucci
5th Italian Conference in Robotics and Intelligent Machines (I-RIM), 2023
📂 Dataset
The dataset is related to a subject (S01) acquisition with 5 trials. Each trial folder is organized as follows:
a .tdf file containing the EMG acquisition via BTS Engineering surface… See the full description on the dataset page: https://huggingface.co/datasets/ami-iit/paper_latella_2023_irim_muscle-force-estimation_dataset.