# Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles

arXiv: https://arxiv.org/abs/2301.10384 · HTML: https://mlsanity.com/paper/2301.10384/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [Interactive Imitation Learning in Robotics: A Survey](https://mlsanity.com/paper/2211.00600/) (2022)
- [Optimization-Based Autonomous Racing of 1:43 Scale RC Cars](https://mlsanity.com/paper/1711.07300/) (2017)
- [Search-based optimal motion planning for automated driving](https://mlsanity.com/paper/1803.04868/) (2018)
- [High-speed Autonomous Drifting with Deep Reinforcement Learning](https://mlsanity.com/paper/2001.01377/) (2020)
- [A Non-Cooperative Game Approach to Autonomous Racing](https://mlsanity.com/paper/1712.03913/) (2017)
- [Search-based Motion Planning for Aggressive Flight in SE(3)](https://mlsanity.com/paper/1710.02748/) (2017)
- [Lazy Receding Horizon A* for Efficient Path Planning in Graphs with Expensive-to-Evaluate Edges](https://mlsanity.com/paper/1803.04998/) (2018)
- [Learning from Experience for Rapid Generation of Local Car Maneuvers](https://mlsanity.com/paper/2012.03707/) (2020)
- [Design of an Autonomous Racecar: Perception, State Estimation and System Integration](https://mlsanity.com/paper/1804.03252/) (2018)
- [Search-Based Motion Planning for Performance Autonomous Driving](https://mlsanity.com/paper/1907.07825/) (2019)
- [A novel approach to model exploration for value function learning](https://mlsanity.com/paper/1906.02789/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Survey of World Models for Autonomous Driving](https://mlsanity.com/paper/2501.11260/) (2025)
- [A Multi-Heuristic Search-based Motion Planning for Automated Parking](https://mlsanity.com/paper/2307.07857/) (2023)
- [Track-Guided Hierarchical Reinforcement Learning for Autonomous Vehicle Drifting with Minimum-Lap-Time Planning](https://mlsanity.com/paper/2608.00113/) (2026)

## Abstract (arXiv)

To achieve optimal robot behavior in dynamic scenarios we need to consider complex dynamics in a predictive manner. In the vehicle dynamics community, it is well know that to achieve time-optimal driving on low surface, the vehicle should utilize drifting. Hence many authors have devised rules to split circuits and employ drifting on some segments. These rules are suboptimal and do not generalize to arbitrary circuit shapes (e.g., S-like curves). So, the question "When to go into which mode and how to drive in it?" remains unanswered. To choose the suitable mode (discrete decision), the algorithm needs information about the feasibility of the continuous motion in that mode. This makes it a class of Task and Motion Planning (TAMP) problems, which are known to be hard to solve optimally in real-time. In the AI planning community, search methods are commonly used. However, they cannot be directly applied to TAMP problems due to the continuous component. Here, we present a search-based method that effectively solves this problem and efficiently searches in a highly dimensional state space with nonlinear and unstable dynamics. The space of the possible trajectories is explored by sampling different combinations of motion primitives guided by the search. Our approach allows to use multiple locally approximated models to generate motion primitives (e.g., learned models of drifting) and effectively simplify the problem without losing accuracy. The algorithm performance is evaluated in simulated driving on a mixed-track with segments of different curvatures (right and left). Our code is available at https://git.io/JenvB