  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. To kickstart the benchmark, we extended several
state-of-the art methods to bimanual manipulation and also present a
language-conditioned behavioral cloning agent -- PerAct2, which enables the
learning and execution of bimanual 6-DoF manipulation tasks. Our novel network
architecture efficiently integrates language processing with action prediction,
allowing robots to understand and perform complex bimanual tasks in response to
user-specified goals. Project website with code is available at:
http://bimanual.github.io
