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Real-time Model Predictive Control with Zonotope-Based Neural Networks for Biped
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https://www.youtube.com/watch?v=w5cg66GQPUQ Real-time Model Predictive Control with Zonotope-Based Neural Networks for Bipedal Social Navigation Abdulaziz Shamsah, Krishanu Agarwal, Shreyas Kousik, Ye Zhao 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. paper link: https://arxiv.org/abs/2403.16485
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