  Learning to lift and rotate objects with the fingertips is necessary for
autonomous in-hand dexterous manipulation. In our study, we explore the impact
of various factors on successful learning strategies for this task.
Specifically, we investigate the role of curriculum learning and haptic
feedback in enabling the learning of dexterous manipulation. Using model-free
Reinforcement Learning, we compare different curricula and two haptic
information modalities (No-tactile vs. 3D-force sensing) for lifting and
rotating a ball against gravity with a three-fingered simulated robotic hand
with no visual input. Note that our best results were obtained when we used a
novel curriculum-based learning rate scheduler, which adjusts the
linearly-decaying learning rate when the reward is changed as it accelerates
convergence to higher rewards. Our findings demonstrate that the choice of
curriculum greatly biases the acquisition of different features of dexterous
manipulation. Surprisingly, successful learning can be achieved even in the
absence of tactile feedback, challenging conventional assumptions about the
necessity of haptic information for dexterous manipulation tasks. We
demonstrate the generalizability of our results to balls of different weights
and sizes, underscoring the robustness of our learning approach. This work,
therefore, emphasizes the importance of the choice curriculum and challenges
long-held notions about the need for tactile information to autonomously learn
in-hand dexterous manipulation.
