  Robotic manipulation holds the potential to replace humans in the execution
of tedious or dangerous tasks. However, control-based approaches are not
suitable due to the difficulty of formally describing open-world manipulation
in reality, and the inefficiency of existing learning methods. Thus, applying
manipulation in a wide range of scenarios presents significant challenges. In
this study, we propose a novel method for skill learning in robotic
manipulation called Tactile Active Inference Reinforcement Learning
(Tactile-AIRL), aimed at achieving efficient training. To enhance the
performance of reinforcement learning (RL), we introduce active inference,
which integrates model-based techniques and intrinsic curiosity into the RL
process. This integration improves the algorithm's training efficiency and
adaptability to sparse rewards. Additionally, we utilize a vision-based tactile
sensor to provide detailed perception for manipulation tasks. Finally, we
employ a model-based approach to imagine and plan appropriate actions through
free energy minimization. Simulation results demonstrate that our method
achieves significantly high training efficiency in non-prehensile objects
pushing tasks. It enables agents to excel in both dense and sparse reward tasks
with just a few interaction episodes, surpassing the SAC baseline. Furthermore,
we conduct physical experiments on a gripper screwing task using our method,
which showcases the algorithm's rapid learning capability and its potential for
practical applications.
