# Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics

arXiv: https://arxiv.org/abs/2002.06599 · HTML: https://mlsanity.com/paper/2002.06599/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2020.

## Built on by (most cited first, 21 of 21)

- [Motion Policy Networks](https://mlsanity.com/paper/2210.12209/) (2022)
- [Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks](https://mlsanity.com/paper/2405.01534/) (2024)
- [EDMP: Ensemble-of-costs-guided Diffusion for Motion Planning](https://mlsanity.com/paper/2309.11414/) (2023)
- [Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models](https://mlsanity.com/paper/2412.19948/) (2024)
- [Neural MP: A Generalist Neural Motion Planner](https://mlsanity.com/paper/2409.05864/) (2024)
- [Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT](https://mlsanity.com/paper/2304.11018/) (2023)
- [Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments](https://mlsanity.com/paper/2509.06953/) (2025)
- [USA-Net: Unified Semantic and Affordance Representations for Robot Memory](https://mlsanity.com/paper/2304.12164/) (2023)
- [PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning](https://mlsanity.com/paper/2408.04054/) (2024)
- [Cascaded Diffusion Models for Neural Motion Planning](https://mlsanity.com/paper/2505.15157/) (2025)
- [Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction](https://mlsanity.com/paper/2203.06963/) (2022)
- [Global Tensor Motion Planning](https://mlsanity.com/paper/2411.19393/) (2024)
- [sbp-env: Sampling-based Motion Planners' Testing Environment](https://mlsanity.com/paper/2110.08402/) (2021)
- [aUToPath: Unified Planning and Control for Autonomous Vehicles in Urban Environments Using Hybrid Lattice and Free-Space Search](https://mlsanity.com/paper/2505.09475/) (2025)
- [Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities](https://mlsanity.com/paper/2603.24318/) (2026)
- [PACE: Adaptive Budget Allocation for Time-Efficient Embodied Planning](https://mlsanity.com/paper/2608.03034/) (2026)
- [Keypoint-based Diffusion for Robotic Motion Planning on the NICOL Robot](https://mlsanity.com/paper/2509.04076/) (2025)
- [Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulation](https://mlsanity.com/paper/2604.05544/) (2026)
- [Sum of Costs Diffusion with Dynamic Guidance for Motion Planning](https://mlsanity.com/paper/2605.24690/) (2026)
- [ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients](https://mlsanity.com/paper/2607.00215/) (2026)
- [NeurRAFT: Robot Motion Planning via Anchor-Level Flow Matching with Clearance-Aware Preference Tuning](https://mlsanity.com/paper/2608.24026/) (2026)

## Abstract (arXiv)

Informed sampling-based planning algorithms exploit problem knowledge for better search performance. This knowledge is often expressed as heuristic estimates of solution cost and used to order the search. The practical improvement of this informed search depends on the accuracy of the heuristic. Selecting an appropriate heuristic is difficult. Heuristics applicable to an entire problem domain are often simple to define and inexpensive to evaluate but may not be beneficial for a specific problem instance. Heuristics specific to a problem instance are often difficult to define or expensive to evaluate but can make the search itself trivial. This paper presents Adaptively Informed Trees (AIT*), an almost-surely asymptotically optimal sampling-based planner based on BIT*. AIT* adapts its search to each problem instance by using an asymmetric bidirectional search to simultaneously estimate and exploit a problem-specific heuristic. This allows it to quickly find initial solutions and converge towards the optimum. AIT* solves the tested problems as fast as RRT-Connect while also converging towards the optimum.