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Found 9 papers, 1 papers with code
Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers
Previous robot learning methods often collect data to train with one specific embodiment for one task, which is expensive and prone to overfitting.
Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks
FoTa is the largest and most diverse dataset in tactile sensing to date and it is made publicly available in a unified format.
PoCo: Policy Composition from and for Heterogeneous Robot Learning
Training general robotic policies from heterogeneous data for different tasks is a significant challenge.
GelSight Svelte: A Human Finger-shaped Single-camera Tactile Robot Finger with Large Sensing Coverage and Proprioceptive Sensing
Moreover, existing methods to estimate proprioceptive information such as total forces and torques applied on the finger from camera-based tactile sensors are not effective when the contact geometry is complex.
FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback
FingerSLAM is constructed with two constituent pose estimators: a multi-pass refined tactile-based pose estimator that captures movements from detailed local textures, and a single-pass vision-based pose estimator that predicts from a global view of the object.
Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation
Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e. g., sliding an object to a goal pose while maintaining contact with a table.
Towards Robotic Assembly by Predicting Robust, Precise and Task-oriented Grasps
Robust task-oriented grasp planning is vital for autonomous robotic precision assembly tasks.
Towards Precise Robotic Grasping by Probabilistic Post-grasp Displacement Estimation
Precise robotic grasping is important for many industrial applications, such as assembly and palletizing, where the location of the object needs to be controlled and known.
Annotation and Detection of Emotion in Text-based Dialogue Systems with CNN
Knowledge of users' emotion states helps improve human-computer interaction.
