  The rise of simulation environments has enabled learning-based approaches for
assembly planning, which is otherwise a labor-intensive and daunting task.
Assembling furniture is especially interesting since furniture are intricate
and pose challenges for learning-based approaches. Surprisingly, humans can
solve furniture assembly mostly given a 2D snapshot of the assembled product.
Although recent years have witnessed promising learning-based approaches for
furniture assembly, they assume the availability of correct connection labels
for each assembly step, which are expensive to obtain in practice. In this
paper, we alleviate this assumption and aim to solve furniture assembly with as
little human expertise and supervision as possible. To be specific, we assume
the availability of the assembled point cloud, and comparing the point cloud of
the current assembly and the point cloud of the target product, obtain a novel
reward signal based on two measures: Incorrectness and incompleteness. We show
that our novel reward signal can train a deep network to successfully assemble
different types of furniture. Code and networks available here:
https://github.com/METU-KALFA/AssembleRL
