  We study the problem of learning online packing skills for irregular 3D
shapes, which is arguably the most challenging setting of bin packing problems.
The goal is to consecutively move a sequence of 3D objects with arbitrary
shapes into a designated container with only partial observations of the object
sequence. Meanwhile, we take physical realizability into account, involving
physics dynamics and constraints of a placement. The packing policy should
understand the 3D geometry of the object to be packed and make effective
decisions to accommodate it in the container in a physically realizable way. We
propose a Reinforcement Learning (RL) pipeline to learn the policy. The complex
irregular geometry and imperfect object placement together lead to huge
solution space. Direct training in such space is prohibitively data intensive.
We instead propose a theoretically-provable method for candidate action
generation to reduce the action space of RL and the learning burden. A
parameterized policy is then learned to select the best placement from the
candidates. Equipped with an efficient method of asynchronous RL acceleration
and a data preparation process of simulation-ready training sequences, a mature
packing policy can be trained in a physics-based environment within 48 hours.
Through extensive evaluation on a variety of real-life shape datasets and
comparisons with state-of-the-art baselines, we demonstrate that our method
outperforms the best-performing baseline on all datasets by at least 12.8% in
terms of packing utility.
