  Most successes in robotic manipulation have been restricted to single-arm
robots, which limits the range of solvable tasks to pick-and-place, insertion,
and objects rearrangement. In contrast, dual and multi arm robot platforms
unlock a rich diversity of problems that can be tackled, such as laundry
folding and executing cooking skills. However, developing controllers for
multi-arm robots is complexified by a number of unique challenges, such as the
need for coordinated bimanual behaviors, and collision avoidance amongst
robots. Given these challenges, in this work we study how to solve bi-manual
tasks using reinforcement learning (RL) trained in simulation, such that the
resulting policies can be executed on real robotic platforms. Our RL approach
results in significant simplifications due to using real-time (4Hz) joint-space
control and directly passing unfiltered observations to neural networks
policies. We also extensively discuss modifications to our simulated
environment which lead to effective training of RL policies. In addition to
designing control algorithms, a key challenge is how to design fair evaluation
tasks for bi-manual robots that stress bimanual coordination, while removing
orthogonal complicating factors such as high-level perception. In this work, we
design a Connect Task, where the aim is for two robot arms to pick up and
attach two blocks with magnetic connection points. We validate our approach
with two xArm6 robots and 3D printed blocks with magnetic attachments, and find
that our system has 100% success rate at picking up blocks, and 65% success
rate at the Connect Task.
