Abstract
Dexterous manipulation remains an aspirational goal for autonomous robotic systems, particularly when learning to lift and rotate objects against gravity with intermittent finger contacts. We use model-free reinforcement learning to compare the effect of curriculum (i.e., combinations of lift and rotation tasks) and haptic information (i.e., no-tactile versus 3D-force) on learning with a simulated three-finger robotic hand. In addition, a novel curriculum-based learning rate scheduler accelerates convergence. We demonstrate that the choice of curriculum biases the progression of learning for dexterous manipulation across objects with different weights, sizes, and shapes-underscoring the robustness of our learning approach. Unexpectedly, learning is achieved even in the absence of haptic information. This challenges conventional thinking about task "complexity" and the necessity of haptic information for dexterous manipulation for this task. This work invites the analogy of curriculum learning as a malleable developmental process from a pluripotent state driven by the nature of the learning experience.