Tactile Sensitive NewtonianVAE

Tactile-sensitive Newtonian Variational Autoencoders (NVAEs) are being developed to improve robotic manipulation by integrating tactile sensing with learned world models. Current research focuses on using NVAEs, often in conjunction with other techniques like imitation learning and multimodal sensing (combining vision and touch), to achieve high-accuracy tasks such as object insertion and packing, even with flexible or hazardous objects. This work addresses the limitations of vision-only approaches by incorporating tactile feedback for improved control and robustness, leading to more dexterous and adaptable robots for industrial and other applications. The resulting advancements promise significant improvements in robotic dexterity and safety in contact-rich manipulation tasks.