PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation Tasks

Understand

Bimanual manipulation is challenging due to precise spatial and temporal coordination required between two arms.

• While there exist several real-world bimanual systems, there is a lack of simulated benchmarks with a large task diversity for systematically studying bimanual capabilities across a wide range of tabletop tasks.
• This paper addresses the gap by extending RLBench to bimanual manipulation.
• We open-source our code and benchmark comprising 13 new tasks with 23 unique task variations, each requiring a high degree of coordination and adaptability.