Mais Jamal

PhD in AI and ML

Moscow, Russia

I am drawn to the moment when a machine must decide under uncertainty — not with perfect information, but with good enough reasoning to act safely. That tension between completeness and real-time feasibility has shaped everything I have built.

Professionally, I am a Behavior Planning and Autonomy Engineer with a Ph.D. in Artificial Intelligence and Machine Learning from the Moscow Institute of Physics and Technology (MIPT), focused on adaptive planning and real-time decision-making under safety constraints. My work centers on enabling machines not only to execute predefined actions, but to anticipate, adapt, and reason in dynamic environments.

My doctoral research resulted in a task-and-motion planning (TAMP) framework extended with QCNet-based multi-agent trajectory prediction, enabling prediction-aware behavior planning in complex, uncertain settings. I design decision-making architectures that integrate planning, perception, and execution, with an emphasis on robustness and real-world feasibility.

I have developed and deployed behavior planning algorithms (behavior trees, reinforcement learning, and heuristic search) for autonomous robotic platforms, including an outdoor autonomous cleaning robot operating in unstructured environments. To support end-to-end validation, I implemented a bidirectional interface between the CARLA simulator and the Apollo Auto platform, enabling seamless transitions from simulation to hardware-in-the-loop testing and full-scale vehicle deployment. My technical expertise includes ROS/ROS2, navigation stack development, system-level integration, and full autonomy pipelines from algorithm design to field validation.

With academic training spanning learning-based robotic manipulation (M.Sc., Mechatronics and Robotic Systems, Bauman Moscow State Technical University) and autonomous aerial systems (B.Sc., Mechatronics, Robotics, and Automation Engineering, Tishreen University), I bring a comprehensive background across ground and aerial robotics. I am particularly motivated by advancing intelligent autonomy in safety-critical, real-world applications where adaptive decision-making and system reliability are essential.

Outside of research, I find myself tracing origins; how languages diverged from ancient roots, how civilizations built and lost knowledge, and how human understanding of science and engineering has transformed across time.

Robotics needs more women — as researchers, engineers, and leaders.

Currently

🔍 Exploring how prediction uncertainty should propagate into PDDL-based planners
🤞 Preparing to relocate to Europe to work on autonomous systems
⏳ Learning German — just getting started

News

Nov 01, 2025	My paper, “Принятие решений о проведении маневра с предсказанием траекторных потоков для беспилотного транспортного средства” (“Maneuver Decision-Making with Trajectory Streams Prediction for Autonomous Vehicles”), has been published in the proceedings of the XXII Russian Conference on Artificial Intelligence (RCAI-2025), held on October 6–10, 2025.
Dec 16, 2024	I defended my doctoral thesis entitled “Development and Research of Adaptive Planning Algorithms and Methods for Autonomous Vehicle Maneuvers.”

Nov 01, 2025

My paper, “Принятие решений о проведении маневра с предсказанием траекторных потоков для беспилотного транспортного средства” (“Maneuver Decision-Making with Trajectory Streams Prediction for Autonomous Vehicles”), has been published in the proceedings of the XXII Russian Conference on Artificial Intelligence (RCAI-2025), held on October 6–10, 2025.

Dec 16, 2024

I defended my doctoral thesis entitled “Development and Research of Adaptive Planning Algorithms and Methods for Autonomous Vehicle Maneuvers.”

Selected publications

AdaptBehaviorTree
Adaptive maneuver planning for autonomous vehicles using behavior tree on apollo platform

Mais Jamal and Aleksandr Panov

In Artificial Intelligence XXXVIII, 2021

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In safety-critical systems such as autonomous driving systems, behavior planning is a significant challenge. The presence of numerous dynamic obstacles makes the driving environment unpredictable. The planning algorithm should be safe, reactive, and adaptable to environmental changes. The paper presents an adaptive maneuver planning algorithm based on an evolving behavior tree created with genetic programming. In addition, we make a technical contribution to the Baidu Apollo autonomous driving platform, allowing the platform to test and develop overtaking maneuver planning algorithms.
@inproceedings{jamal2021adaptive, title = {Adaptive maneuver planning for autonomous vehicles using behavior tree on apollo platform}, author = {Jamal, Mais and Panov, Aleksandr}, booktitle = {Artificial Intelligence XXXVIII}, publisher = {Springer, Cham}, address = {Cham}, volume = {13101}, pages = {327--340}, year = {2021}, isbn = {978-3-030-91100-3}, doi = {10.1007/978-3-030-91100-3_26}, url = {https://link.springer.com/chapter/10.1007/978-3-030-91100-3_26}, }
FFStreams_RAL
FFStreams: Fast Search with Streams for Autonomous Maneuver Planning

Mais Jamal and Aleksandr Panov

IEEE Robotics and Automation Letters, 2024

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In autonomous driving, maneuver planning is essential for ride safety and comfort, involving both motion planning and decision-making. This paper introduces FFStreams, a novel approach combining high-level decision-making and low-level motion planning to solve maneuver planning problems while considering kinematic constraints. Addressed as an integrated Task and Motion Planning (TAMP) problem in a dynamic environment, the planner utilizes PDDL, incorporates Streams, and employs Fast-Forward heuristic search. Evaluated against baseline methods in challenging overtaking and lane-changing scenarios, FFStreams demonstrates superior performance, highlighting its potential for real-world applications.
@article{Jamal2024, title = {FFStreams: Fast Search with Streams for Autonomous Maneuver Planning}, volume = {9}, url = {https://ieeexplore.ieee.org/document/10552884}, doi = {10.1109/LRA.2024.3412633}, pages = {6752--6759}, number = {7}, journal = {IEEE Robotics and Automation Letters}, author = {Jamal, Mais and Panov, Aleksandr}, year = {2024}, }
FFStreams++_Predict
Maneuver Decision-Making with Trajectory Streams Prediction for Autonomous Vehicles

Mais Jamal and Aleksandr Panov

2024

Abs Bib HTML PDF

Decision-making, motion planning, and trajectory prediction are crucial in autonomous driving systems. By accurately forecasting the movements of other road users, the decision-making capabilities of the autonomous system can be enhanced, making it more effective in responding to dynamic and unpredictable environments and more adaptive to diverse road scenarios. This paper presents the FFStreams++ approach for decision-making and motion planning of different maneuvers, including unprotected left turn, overtaking, and keep-lane. FFStreams++ is a combination of sampling-based and search-based approaches, where iteratively new sampled trajectories for different maneuvers are generated and optimized, and afterward, a heuristic search planner is called, searching for an optimal plan. We model the autonomous diving system in the Planning Domain Definition Language (PDDL) and search for the optimal plan using a heuristic Fast-Forward planner. In this approach, the initial state of the problem is modified iteratively through streams, which will generate maneuver-specific trajectory candidates, increasing the iterating level until an optimal plan is found. FFStreams++ integrates a query-connected network model for predicting possible future trajectories for each surrounding obstacle along with their probabilities. The proposed approach was tested on the CommonRoad simulation framework. We use a collection of randomly generated driving scenarios for overtaking and unprotected left turns at intersections to evaluate the FFStreams++ planner. The test results confirmed that the proposed approach can effectively execute various maneuvers to ensure safety and reduce the risk of collisions with nearby traffic agents.
@article{jamal2024maneuver, title = {Maneuver Decision-Making with Trajectory Streams Prediction for Autonomous Vehicles}, author = {Jamal, Mais and Panov, Aleksandr}, year = {2024}, archiveprefix = {arXiv}, primaryclass = {cs.RO}, url = {https://arxiv.org/abs/2409.10165}, }