Neural Trajectory Model: Implicit Neural Trajectory Representation for Trajectories Generation

The multi-agent trajectory planning problem is a difficult problem in robotics due to its computational complexity and real-world environment complexity with uncertainty, non-linearity, and real-time requirements. Many existing solutions are either search-based or optimization-based approaches with simplified assumptions of environment, limited planning speed, and limited scalability in the number of agents. In this work, we first attempt to reformulate single-agent and multi-agent trajectory planning problems as query problems over an implicit neural representation of trajectories. We formulate such implicit representations as Neural Trajectory Models (NTM) which can be queried to generate nearly optimal trajectory in complex environments. We conduct experiments in simulation environments and demonstrate that NTM achieve (1) sub-millisecond planning time using GPUs, (2) almost avoiding all collisions, and (3) generating almost shortest paths. We also demonstrate that the same NTM framework can also be used for refining low-quality and conflicting multi-agent trajectories into nearly optimal solutions efficiently. (Open source code is available at https://github.com/laser2099/neural-trajectory-model)