CORN: Contact-based Object Representation for Nonprehensile Manipulation of General Unseen Objects

Understand

Nonprehensile manipulation is essential for manipulating objects that are too thin, large, or otherwise ungraspable in the wild.

• To sidestep the difficulty of contact modeling in conventional modeling-based approaches, reinforcement learning (RL) has recently emerged as a promising alternative.
• However, previous RL approaches either lack the ability to generalize over diverse object shapes, or use simple action primitives that limit the diversity of robot motions.
• Furthermore, using RL over diverse object geometry is challenging due to the high cost of training a policy that takes in high-dimensional sensory inputs.