# ADAPT: An Autonomous Forklift for Construction Site Operation

arXiv: https://arxiv.org/abs/2503.14331 · HTML: https://mlsanity.com/paper/2503.14331/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 102 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 23 of 23)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024)
- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning](https://mlsanity.com/paper/2305.14909/) (2023)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [A Survey on Large Language Model-Based Game Agents](https://mlsanity.com/paper/2404.02039/) (2024)
- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects](https://mlsanity.com/paper/2302.13075/) (2023)
- [AutoGPT+P: Affordance-based Task Planning with Large Language Models](https://mlsanity.com/paper/2402.10778/) (2024)
- [Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots](https://mlsanity.com/paper/2203.01389/) (2022)
- [Behavior Trees and State Machines in Robotics Applications](https://mlsanity.com/paper/2208.04211/) (2022)
- [From the Desks of ROS Maintainers: A Survey of Modern & Capable Mobile Robotics Algorithms in the Robot Operating System 2](https://mlsanity.com/paper/2307.15236/) (2023)
- [Continuous-Time State Estimation Methods in Robotics: A Survey](https://mlsanity.com/paper/2411.03951/) (2024)
- [Reactive Task and Motion Planning under Temporal Logic Specifications](https://mlsanity.com/paper/2103.14464/) (2021)
- [Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning](https://mlsanity.com/paper/2003.07754/) (2020)
- [HEAP -- The autonomous walking excavator](https://mlsanity.com/paper/2106.05059/) (2021)
- [Automated Planning Domain Inference for Task and Motion Planning](https://mlsanity.com/paper/2410.16445/) (2024)
- [Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks](https://mlsanity.com/paper/2501.10080/) (2025)
- [Motion Primitives Planning For Center-Articulated Vehicles](https://mlsanity.com/paper/2405.17127/) (2024)

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

- [Lang2Lift: A Language-Guided Autonomous Forklift System for Outdoor Industrial Pallet Handling](https://mlsanity.com/paper/2508.15427/) (2025)
- [PIRATR: Parametric Object Inference for Robotic Applications with Transformers in 3D Point Clouds](https://mlsanity.com/paper/2602.05557/) (2026)
- [Build on Priors: Vision--Language--Guided Neuro-Symbolic Imitation Learning for Data-Efficient Real-World Robot Manipulation](https://mlsanity.com/paper/2604.03759/) (2026)

## Abstract (arXiv)

Efficient material logistics play a critical role in controlling costs and schedules in the construction industry. However, manual material handling remains prone to inefficiencies, delays, and safety risks. Autonomous forklifts offer a promising solution to streamline on-site logistics, reducing reliance on human operators and mitigating labor shortages. This paper presents the development and evaluation of ADAPT (Autonomous Dynamic All-terrain Pallet Transporter), a fully autonomous off-road forklift designed for construction environments. Unlike structured warehouse settings, construction sites pose significant challenges, including dynamic obstacles, unstructured terrain, and varying weather conditions. To address these challenges, our system integrates AI-driven perception techniques with traditional approaches for decision making, planning, and control, enabling reliable operation in complex environments. We validate the system through extensive real-world testing, comparing its continuous performance against an experienced human operator across various weather conditions. Our findings demonstrate that autonomous outdoor forklifts can operate near human-level performance, offering a viable path toward safer and more efficient construction logistics.