  This study proposes a novel approach based on reinforcement learning (RL) to
enhance the sorting efficiency of scrap metal using delta robots and a
Pick-and-Place (PaP) process, widely used in the industry. We use three
classical model-free RL algorithms (TD3, SAC and PPO) to reduce the time to
sort metal scraps. We learn the release position and speed needed to throw an
object in a bin instead of moving to the exact bin location, as with the
classical PaP technique. Our contribution is threefold. First, we provide a new
simulation environment for learning RL-based Pick-and-Throw (PaT) strategies
for parallel grippers. Second, we use RL algorithms for learning this task in
this environment resulting in 89% accuracy while speeding up the throughput by
51% in simulation. Third, we evaluate the performances of RL algorithms and
compare them to a PaP and a state-of-the-art PaT method both in simulation and
reality, learning only from simulation with domain randomisation and without
fine tuning in reality to transfer our policies. This work shows the benefits
of RL-based PaT compared to PaP or classical optimization PaT techniques used
in the industry.
