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Found 8 papers, 1 papers with code
Multi-task real-robot data with gaze attention for dual-arm fine manipulation
Additionally, this dataset includes visual attention signals as well as dual-action labels, a signal that separates actions into a robust reaching trajectory and precise interaction with objects, and language instructions to achieve robust and precise object manipulation.
Goal-conditioned dual-action imitation learning for dexterous dual-arm robot manipulation
Long-horizon dexterous robot manipulation of deformable objects, such as banana peeling, is a problematic task because of the difficulties in object modeling and a lack of knowledge about stable and dexterous manipulation skills.
Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer
In this study, deep imitation learning is applied to tasks that require force feedback.
Memory-based gaze prediction in deep imitation learning for robot manipulation
We propose that gaze prediction from sequential visual input enables the robot to perform a manipulation task that requires memory.
Transformer-based deep imitation learning for dual-arm robot manipulation
Deep imitation learning is promising for solving dexterous manipulation tasks because it does not require an environment model and pre-programmed robot behavior.
Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation
The results of this study demonstrate that a deep imitation learning based method, inspired by the gaze-based dual resolution visuomotor control system in humans, can solve the needle threading task.
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder
Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis.
FAVAE: Sequence Disentanglement using Information Bottleneck Principle
Previous models disentangle static and dynamic factors by explicitly modeling the priors of latent variables to distinguish between these factors.
