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TIAGo RL: Simulated Reinforcement Learning Environments with Tactile Data for Mobile Robots
Tactile information is important for robust performance in robotic tasks that involve physical interaction, such as object manipulation.
Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot
The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks.
Guiding Representation Learning in Deep Generative Models with Policy Gradients
Therefore, this paper investigates the issues of joint training approaches and explores incorporation of policy gradients from RL into the VAE's latent space to find a task-specific latent space representation.
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