# Real-time Model Predictive Control with Zonotope-Based Neural Networks for Bipedal Social Navigation

arXiv: https://arxiv.org/abs/2403.16485 · HTML: https://mlsanity.com/paper/2403.16485/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [EmoBipedNav: Emotion-aware Social Navigation for Bipedal Robots with Deep Reinforcement Learning](https://mlsanity.com/paper/2503.12538/) (2025)

## Abstract (arXiv)

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.