  Social robot navigation (SRN) is a relevant problem that involves navigating
a pedestrian-rich environment in a socially acceptable manner. It is an
essential part of making social robots effective in pedestrian-rich settings.
The use cases of such robots could vary from companion robots to warehouse
robots to autonomous wheelchairs. In recent years, deep reinforcement learning
has been increasingly used in research on social robot navigation. Our work
introduces CAMRL (Context-Aware Mamba-based Reinforcement Learning). Mamba is a
new deep learning-based State Space Model (SSM) that has achieved results
comparable to transformers in sequencing tasks. CAMRL uses Mamba to determine
the robot's next action, which maximizes the value of the next state predicted
by the neural network, enabling the robot to navigate effectively based on the
rewards assigned. We evaluate CAMRL alongside existing solutions (CADRL,
LSTM-RL, SARL) using a rigorous testing dataset which involves a variety of
densities and environment behaviors based on ORCA and SFM, thus, demonstrating
that CAMRL achieves higher success rates, minimizes collisions, and maintains
safer distances from pedestrians. This work introduces a new SRN planner,
showcasing the potential for deep-state space models for robot navigation.
