Real-time Model Predictive Control with Zonotope-Based Neural Networks for Bipedal Social Navigation
<|reference_start|>Real-time Model Predictive Control with Zonotope-Based Neural Networks for Bipedal Social Navigation: This study addresses the challenge of bipedal navigation in a dynamic human-crowded environment, a research area that remains largely underexplored in the field of legged navigation. We propose two cascaded zonotope-based neural networks: a Pedestrian Prediction Network (PPN) for pedestrians' future trajectory prediction and an Ego-agent Social Network (ESN) for ego-agent social path planning. Representing future paths as zonotopes allows for efficient reachability-based planning and collision checking. The ESN is then integrated with a Model Predictive Controller (ESN-MPC) for footstep planning for our bipedal robot Digit designed by Agility Robotics. ESN-MPC solves for a collision-free optimal trajectory by optimizing through the gradients of ESN. ESN-MPC optimal trajectory is sent to the low-level controller for full-order simulation of Digit. The overall proposed framework is validated with extensive simulations on randomly generated initial settings with varying human crowd densities.<|reference_end|>
@article{shamsah2024real-time,
    title={Real-time Model Predictive Control with Zonotope-Based Neural Networks
  for Bipedal Social Navigation},
    author={Abdulaziz Shamsah, Krishanu Agarwal, Shreyas Kousik, and Ye Zhao},
    journal={arXiv preprint arXiv:2403.16485},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16485},
    primaryClass={cs.RO}
}