Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes

Understand

We study the problem of robotic stacking with objects of complex geometry.

• We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple "pick-and-place" solution.
• Our method is a reinforcement learning (RL) approach combined with vision-based interactive policy distillation and simulation-to-reality transfer.
• Our learned policies can efficiently handle multiple object combinations in the real world and exhibit a large variety of stacking skills.