Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning

Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning

In this work, we present a method for obtaining an implicit objective function for vision-based navigation. The proposed methodology relies on Imitation Learning, Model Predictive Control (MPC), and an interpretation technique used in Deep Neural Networks. We use Imitation Learning as a means to do Inverse Reinforcement Learning in order to create an approximate cost function generator for a visual navigation challenge. The resulting cost function, the costmap, is used in conjunction with MPC for real-time control and outperforms other state-of-the-art costmap generators in novel environments.…

多智能体自主赛车中鲁棒时空运动规划 via 拓扑间隙识别与加速MPC

Robust Spatiotemporal Motion Planning for Multi-Agent Autonomous Racing via Topological Gap Identification and Accelerated MPC

本文提出基于拓扑间隙识别与加速MPC的框架，通过预测对手行为和动态占用走廊，实现高速多智能体自主赛车中鲁棒的时空规划与高精度控制。

Nullspace Structure in Model Predictive Control

Robotic tasks can be accomplished by exploiting different forms of redundancies. This work focuses on planning redundancy within Model Predictive Control (MPC) in which several paths can be considered within the MPC time horizon. We present the nullspace structure in MPC with a quadratic approximation of the cost and a linearization of the dynamics. We exploit the low rank structure of the precision matrices used in MPC (encapsulating spatiotemporal information) to perform hierarchical task planning, and show how nullspace computation can be treated as a fusion problem (computed with a product…

动态神经势场：在移动障碍物存在下的在线轨迹优化

Dynamic Neural Potential Field: Online Trajectory Optimization in the Presence of Moving Obstacles

本文提出动态神经势场模型，结合传统优化与Transformer预测器，实现高效安全的轨迹优化，在动态环境中表现更优。