  Motion planning is the soul of robot decision making. Classical planning
algorithms like graph search and reaction-based algorithms face challenges in
cases of dense and dynamic obstacles. Deep learning algorithms generate
suboptimal one-step predictions that cause many collisions. Reinforcement
learning algorithms generate optimal or near-optimal time-sequential
predictions. However, they suffer from slow convergence, suboptimal converged
results, and overfittings. This paper introduces a hybrid algorithm for robotic
motion planning: long short-term memory (LSTM) pooling and skip connection for
attention-based discrete soft actor critic (LSA-DSAC). First, graph network
(relational graph) and attention network (attention weight) interpret the
environmental state for the learning of the discrete soft actor critic
algorithm. The expressive power of attention network outperforms that of graph
in our task by difference analysis of these two representation methods.
However, attention based DSAC faces the overfitting problem in training.
Second, the skip connection method is integrated to attention based DSAC to
mitigate overfitting and improve convergence speed. Third, LSTM pooling is
taken to replace the sum operator of attention weigh and eliminate overfitting
by slightly sacrificing convergence speed at early-stage training. Experiments
show that LSA-DSAC outperforms the state-of-the-art in training and most
evaluations. The physical robot is also implemented and tested in the real
world.

Robotics;Artificial Intelligence