From Human Hands to Robotic Limbs: A Study in Motor Skill Embodiment for Telemanipulation
This addresses telemanipulation challenges for robotics applications, but it is incremental as it builds on existing VAE and mapping techniques.
The paper tackles the problem of teleoperating a redundant robot manipulator using human arm gestures by proposing a GRU-based Variational Autoencoder to learn a latent representation of the manipulator's configuration space, enabling real-time trajectory generation and novel configurations from unseen human features.
This paper presents a teleoperation system for controlling a redundant degree of freedom robot manipulator using human arm gestures. We propose a GRU-based Variational Autoencoder to learn a latent representation of the manipulator's configuration space, capturing its complex joint kinematics. A fully connected neural network maps human arm configurations into this latent space, allowing the system to mimic and generate corresponding manipulator trajectories in real time through the VAE decoder. The proposed method shows promising results in teleoperating the manipulator, enabling the generation of novel manipulator configurations from human features that were not present during training.