  Crowd navigation has received increasing attention from researchers over the
last few decades, resulting in the emergence of numerous approaches aimed at
addressing this problem to date. Our proposed approach couples agent motion
prediction and planning to avoid the freezing robot problem while
simultaneously capturing multi-agent social interactions by utilizing a
state-of-the-art trajectory prediction model i.e., social long short-term
memory model (Social-LSTM). Leveraging the output of Social-LSTM for the
prediction of future trajectories of pedestrians at each time-step given the
robot's possible actions, our framework computes the optimal control action
using Model Predictive Control (MPC) for the robot to navigate among
pedestrians. We demonstrate the effectiveness of our proposed approach in
multiple scenarios of simulated crowd navigation and compare it against several
state-of-the-art reinforcement learning-based methods.
