Aleksandr I. Panov

2025

1. AAAI

   MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale

   Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov, and 1 more author

   In AAAI 2025, 2025

   Abs Bib

   Multi-agent pathfinding (MAPF) is a challenging computational problem that typically requires to find collision-free paths for multiple agents in a shared environment. Solving MAPF optimally is NP-hard, yet efficient solutions are critical for numerous applications, including automated warehouses and transportation systems. Recently, learning-based approaches to MAPF have gained attention, particularly those leveraging deep reinforcement learning. Following current trends in machine learning, we have created a foundation model for the MAPF problems called MAPF-GPT. Using imitation learning, we have trained a policy on a set of precollected sub-optimal expert trajectories that can generate actions in conditions of partial observability without additional heuristics, reward functions, or communication with other agents. The resulting MAPF-GPT model demonstrates zero-shot learning abilities when solving the MAPF problem instances that were not present in the training dataset. We show that MAPF-GPT notably outperforms the current best-performing learnable-MAPF solvers on a diverse range of problem instances and is efficient in terms of computation (in the inference mode).

   @inproceedings{andreychuk_mapf-gpt_2025,
     dimensions = {true},
     title = {{MAPF}-{GPT}: {Imitation} {Learning} for {Multi}-{Agent} {Pathfinding} at {Scale}},
     booktitle = {{AAAI} 2025},
     author = {Andreychuk, Anton and Yakovlev, Konstantin and Panov, Aleksandr and Skrynnik, Alexey},
     year = {2025}
   }

2. AIJ

   Generative Models for Grid-Based and Image-Based Pathfinding

   Daniil Kirilenko, Anton Andreychuk, Aleksandr I Panov, and 1 more author

   Artificial Intelligence, 2025

   Abs Bib

   Pathfinding is a challenging problem which generally asks to find a sequence of valid moves for an agent provided with a representation of the environment, i.e. a map, in which it operates. In this work, we consider pathfinding on binary grids and on image representations of the digital elevation models. In the former case, the transition costs are known, while in latter scenario, they are not. A widespread method to solve the first problem is to utilize a search algorithm that systematically explores the search space to obtain a solution. Ideally, the search should also be complemented with an informative heuristic to focus on the goal and prune the unpromising regions of the search space, thus decreasing the number of search iterations. Unfortunately, the widespread heuristic functions for grid-based pathfinding, such as Manhattan distance or Chebyshev distance, do not take the obstacles into account and in obstacle-rich environments demonstrate inefficient performance. As for pathfinding with image inputs, the heuristic search cannot be applied straightforwardly as the transition costs, i.e. the costs of moving from one pixel to the other, are not known. To tackle both challenges, we suggest utilizing modern deep neural networks to infer the instance-dependent heuristic functions at the pre-processing step and further use them for pathfinding with standard heuristic search algorithms. The principal heuristic function that we suggest learning is the path probability, which indicates how likely the grid cell (pixel) is lying on the shortest path (for binary grids with known transition costs, we also suggest another variant of the heuristic function that can speed up the search). Learning is performed in a supervised fashion (while we have also explored the possibilities of end-to-end learning that includes a planner in the learning pipeline). At the test time, path probability is used as the secondary heuristic for the Focal Search, a specific heuristic search algorithm that provides the theoretical guarantees on the cost bound of the resultant solution. Empirically, we show that the suggested approach significantly outperforms state-of-the-art competitors in a variety of different tasks (including out-of-the distribution instances).

   @article{kirilenko_generative_2025,
     dimensions = {true},
     title = {Generative Models for Grid-Based and Image-Based Pathfinding},
     volume = {338},
     url = {https://www.sciencedirect.com/science/article/abs/pii/S0004370224001747},
     doi = {10.1016/j.artint.2024.104238},
     journal = {Artificial Intelligence},
     author = {Kirilenko, Daniil and Andreychuk, Anton and Panov, Aleksandr I and Yakovlev, Konstantin},
     year = {2025}
   }

2024

1. ECAI

   Instruction Following with Goal-Conditioned Reinforcement Learning in Virtual Environments

   Zoya Volovikova, Alexey Skrynnik, Petr Kuderov, and 1 more author

   In Frontiers in Artificial Intelligence and Applications, 2024

   Abs Bib

   In this study, we address the issue of enabling an artificial intelligence agent to execute complex language instructions within virtual environments. In our framework, we assume that these instructions involve intricate linguistic structures and multiple interdependent tasks that must be navigated successfully to achieve the desired outcomes. To effectively manage these complexities, we propose a hierarchical framework that combines the deep language comprehension of large language models with the adaptive actionexecution capabilities of reinforcement learning agents: the language module (based on LLM) translates the language instruction into a high-level action plan, which is then executed by a pre-trained reinforcement learning agent.We have demonstrated the effectiveness of our approach in two different environments: in IGLU, where agents are instructed to build structures, and in Crafter, where agents perform tasks and interact with objects in the surrounding environment according to language commands.

   @inproceedings{volovikova_instruction_2024,
     dimensions = {true},
     title = {Instruction Following with Goal-Conditioned Reinforcement Learning in Virtual Environments},
     volume = {392},
     url = {https://ebooks.iospress.nl/volumearticle/69640},
     doi = {10.3233/FAIA240545},
     pages = {650--657},
     booktitle = {Frontiers in Artificial Intelligence and Applications},
     author = {Volovikova, Zoya and Skrynnik, Alexey and Kuderov, Petr and Panov, Aleksandr I},
     year = {2024}
   }

2. RAL

   FFStreams: Fast Search with Streams for Autonomous Maneuver Planning

   Mais Jamal, and Aleksandr Panov

   IEEE Robotics and Automation Letters, 2024

   Abs Bib PDF

   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 lanechanging scenarios, FFStreams demonstrates superior performance, highlighting its potential for real-world applications.

   @article{Jamal2024,
     dimensions = {true},
     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}
   }

3. ICRA

   Neural Potential Field for Obstacle-Aware Local Motion Planning

   Muhammad Alhaddad, Konstantin Mironov, Aleksey Staroverov, and 1 more author

   In 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024

   Abs Bib PDF

   Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case when both the obstacle map and robot footprint are arbitrary. We propose a Neural Potential Field: a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, and robot footprint. The differentiability of our model allows its usage within the MPC solver. It is computationally hard to solve problems with a very high number of parameters. Therefore, our architecture includes neural image encoders, which transform obstacle maps and robot footprints into embeddings, which reduce problem dimensionality by two orders of magnitude. The reference data for network training are generated based on algorithmic calculation of a signed distance function. Comparative experiments showed that the proposed approach is comparable with existing local planners: it provides trajectories with outperforming smoothness, comparable path length, and safe distance from obstacles. Experiment on Husky UGV mobile robot showed that our approach allows real-time and safe local planning. The code for our approach is presented at https://github.com/cog-isa/NPField together with demo video.

   @inproceedings{Alhaddad2024,
     dimensions = {true},
     title = {Neural Potential Field for Obstacle-Aware Local Motion Planning},
     booktitle = {2024 IEEE International Conference on Robotics and Automation (ICRA)},
     author = {Alhaddad, Muhammad and Mironov, Konstantin and Staroverov, Aleksey and Panov, Aleksandr},
     year = {2024},
     keywords = {myconf, frccsc, mipt, airi, confastar}
   }

4. AAAI

   Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding

   Alexey Skrynnik, Anton Andreychuk, Konstantin Yakovlev, and 1 more author

   In Proceedings of the AAAI Conference on Artificial Intelligence, 2024

   Abs Bib PDF

   The Multi-Agent Pathfinding (MAPF) problem involves finding a set of conflict-free paths for a group of agents confined to a graph. In typical MAPF scenarios, the graph and the agents’ starting and ending vertices are known beforehand, allowing the use of centralized planning algorithms. However, in this study, we focus on the decentralized MAPF setting, where the agents may observe the other agents only locally and are restricted in communications with each other. Specifically, we investigate the lifelong variant of MAPF, where new goals are continually assigned to the agents upon completion of previous ones. Drawing inspiration from the successful AlphaZero approach, we propose a decentralized multi-agent Monte Carlo Tree Search (MCTS) method for MAPF tasks. Our approach utilizes the agent’s observations to recreate the intrinsic Markov decision process, which is then used for planning with a tailored for multi-agent tasks version of neural MCTS. The experimental results show that our approach outperforms state-of-theart learnable MAPF solvers. The source code is available at https://github.com/AIRI-Institute/mats-lp.

   @inproceedings{Skrynnik2024b,
     dimensions = {true},
     title = {Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding},
     volume = {38},
     url = {https://ojs.aaai.org/index.php/AAAI/article/view/29703},
     doi = {10.1609/aaai.v38i16.29703},
     pages = {17531--17540},
     booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
     author = {Skrynnik, Alexey and Andreychuk, Anton and Yakovlev, Konstantin and Panov, Aleksandr},
     year = {2024},
     keywords = {myconf, scopus, frccsc, airi, confastar}
   }

5. AAAI

   Learn to Follow: Decentralized Lifelong Multi-Agent Pathfinding via Planning and Learning

   Alexey Skrynnik, Anton Andreychuk, Maria Nesterova, and 2 more authors

   In Proceedings of the AAAI Conference on Artificial Intelligence, 2024

   Abs Bib PDF

   Multi-agent Pathfinding (MAPF) problem generally asks to find a set of conflict-free paths for a set of agents confined to a graph and is typically solved in a centralized fashion. Conversely, in this work, we investigate the decentralized MAPF setting, when the central controller that possesses all the information on the agents’ locations and goals is absent and the agents have to sequentially decide the actions on their own without having access to the full state of the environment. We focus on the practically important lifelong variant of MAPF, which involves continuously assigning new goals to the agents upon arrival to the previous ones. To address this complex problem, we propose a method that integrates two complementary approaches: planning with heuristic search and reinforcement learning through policy optimization. Planning is utilized to construct and re-plan individual paths. We enhance our planning algorithm with a dedicated technique tailored to avoid congestion and increase the throughput of the system. We employ reinforcement learning to discover the collision avoidance policies that effectively guide the agents along the paths. The policy is implemented as a neural network and is effectively trained without any reward-shaping or external guidance. We evaluate our method on a wide range of setups comparing it to the state-of-the-art solvers. The results show that our method consistently outperforms the learnable competitors, showing higher throughput and better ability to generalize to the maps that were unseen at the training stage. Moreover our solver outperforms a rulebased one in terms of throughput and is an order of magnitude faster than a state-of-the-art search-based solver. The code is available at https://github.com/AIRI-Institute/learn-to-follow.

   @inproceedings{Skrynnik2024,
     dimensions = {true},
     title = {Learn to Follow: Decentralized Lifelong Multi-Agent Pathfinding via Planning and Learning},
     volume = {38},
     url = {https://ojs.aaai.org/index.php/AAAI/article/view/29704},
     pages = {17541--17549},
     booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
     author = {Skrynnik, Alexey and Andreychuk, Anton and Nesterova, Maria and Yakovlev, Konstantin and Panov, Aleksandr},
     year = {2024},
     keywords = {myconf, scopus, mipt, airi, 70-2021-00138, confastar}
   }

6. ICLR

   Object-Centric Learning with Slot Mixture Module

   Daniil Kirilenko, Vitaliy Vorobyov, Alexey Kovalev, and 1 more author

   In The Twelfth International Conference on Learning Representations, 2024

   Abs Bib PDF

   Object-centric architectures usually apply a differentiable module to the entire feature map to decompose it into sets of entity representations called slots. Some of these methods structurally resemble clustering algorithms, where the cluster’s center in latent space serves as a slot representation. Slot Attention is an example of such a method, acting as a learnable analog of the soft k-means algorithm. Our work employs a learnable clustering method based on the Gaussian Mixture Model. Unlike other approaches, we represent slots not only as centers of clusters but also incorporate information about the distance between clusters and assigned vectors, leading to more expressive slot representations. Our experiments demonstrate that using this approach instead of Slot Attention improves performance in object-centric scenarios, achieving state-of-the-art results in the set property prediction task.

   @inproceedings{Kirilenko2024,
     dimensions = {true},
     title = {Object-Centric Learning with Slot Mixture Module},
     url = {https://openreview.net/forum?id=aBUidW4Nkd},
     booktitle = {The Twelfth International Conference on Learning Representations},
     author = {Kirilenko, Daniil and Vorobyov, Vitaliy and Kovalev, Alexey and Panov, Aleksandr},
     year = {2024},
     keywords = {myconf, frccsc, mipt, 20-71-10116, airi, confastar}
   }

7. ICLR

   Gradual Optimization Learning for Conformational Energy Minimization

   Artem Tsypin, Leonid Ugadiarov, Kuzma Khrabrov, and 7 more authors

   In The Twelfth International Conference on Learning Representations, 2024

   Bib PDF

   @inproceedings{Tsypin2024,
     dimensions = {true},
     title = {Gradual Optimization Learning for Conformational Energy Minimization},
     url = {https://openreview.net/forum?id=FMMF1a9ifL},
     booktitle = {The Twelfth International Conference on Learning Representations},
     author = {Tsypin, Artem and Ugadiarov, Leonid and Khrabrov, Kuzma and Telepov, Alexander and Rumiantsev, Egor and Skrynnik, Alexey and Panov, Aleksandr and Vetrov, Dmitry and Tutubalina, Elena and Kadurin, Artur},
     year = {2024},
     keywords = {myconf, frccsc, airi, confastar}
   }

8. EAAI

   Hierarchical waste detection with weakly supervised segmentation in images from recycling plants

   Dmitry Yudin, Nikita Zakharenko, Artem Smetanin, and 7 more authors

   Engineering Applications of Artificial Intelligence, 2024

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   Reducing environmental pollution with household waste and emissions from the computing clusters is an urgent technological problem. In our work, we explore both of these aspects: the deep learning application to improve the efficiency of waste recognition on recycling plant’s conveyor, as well as carbon dioxide emission from the computing devices used in this process. To conduct research, we developed an unique open WaRP dataset that demonstrates the best diversity among similar industrial datasets and contains more than 10,000 images with 28 different types of recyclable goods (bottles, glasses, card boards, cans, detergents, and canisters). Objects can overlap, be in poor lighting conditions, or significantly distorted. On the WaRP dataset, we study training and evaluation of cutting-edge deep neural networks for detection, classification and segmentation tasks. Additionally, we developed a hierarchical neural network approach called H-YC with weakly supervised waste segmentation. It provided a notable increase in the detection quality and made it possible to segment images, learning only having class labels, not their masks. Both the suggested hierarchical approach and the WaRP dataset have shown great industrial application potential.

   @article{Yudin2024,
     dimensions = {true},
     title = {Hierarchical waste detection with weakly supervised segmentation in images from recycling plants},
     volume = {128},
     issn = {0952-1976},
     url = {https://linkinghub.elsevier.com/retrieve/pii/S0952197623017268},
     doi = {10.1016/j.engappai.2023.107542},
     pages = {107542},
     journal = {Engineering Applications of Artificial Intelligence},
     author = {Yudin, Dmitry and Zakharenko, Nikita and Smetanin, Artem and Filonov, Roman and Kichik, Margarita and Kuznetsov, Vladislav and Larichev, Dmitry and Gudov, Evgeny and Budennyy, Semen and Panov, Aleksandr},
     urldate = {2023-11-20},
     year = {2024},
     keywords = {mypub, scopus, mipt, airi, q1scopusprelim}
   }

9. Access

   Interactive Semantic Map Representation for Skill-Based Visual Object Navigation

   Tatiana Zemskova, Aleksei Staroverov, Kirill Muravyev, and 2 more authors

   IEEE Access, 2024

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   Visual object navigation is one of the key tasks in mobile robotics. One of the most important components of this task is the accurate semantic representation of the scene, which is needed to determine and reach a goal object. This paper introduces a new representation of a scene semantic map formed during the embodied agent interaction with the indoor environment. It is based on a neural network method that adjusts the weights of the segmentation model with backpropagation of the predicted fusion loss values during inference on a regular (backward) or delayed (forward) image sequence. We implement this representation into a full-fledged navigation approach called SkillTron. The method can select robot skills from end-to-end policies based on reinforcement learning and classic map-based planning methods. The proposed approach makes it possible to form both intermediate goals for robot exploration and the final goal for object navigation. We conduct intensive experiments with the proposed approach in the Habitat environment, demonstrating its significant superiority over state-of-the-art approaches in terms of navigation quality metrics. The developed code and custom datasets are publicly available at github.com/AIRI-Institute/skill-fusion.

   @article{Zemskova2024,
     dimensions = {true},
     title = {Interactive Semantic Map Representation for Skill-Based Visual Object Navigation},
     volume = {12},
     issn = {2169-3536},
     url = {https://ieeexplore.ieee.org/document/10477345},
     doi = {10.1109/ACCESS.2024.3380450},
     pages = {44628--44639},
     journal = {{IEEE} Access},
     author = {Zemskova, Tatiana and Staroverov, Aleksei and Muravyev, Kirill and Yudin, Dmitry and Panov, Aleksandr},
     year = {2024},
     keywords = {mypub, scopus, frccsc, mipt, 20-71-10116, airi, q1scopusprelim}
   }

2023

1. Access

   Fine-tuning Multimodal Transformer Models for Generating Actions in Virtual and Real Environments

   Aleksei Staroverov, Andrey S Gorodetsky, Andrei S Krishtopik, and 3 more authors

   IEEE Access, 2023

   Abs Bib PDF

   In this work, we propose and investigate an original approach to using a pre-trained multimodal transformer of a specialized architecture for controlling a robotic agent in an object manipulation task based on language instruction, which we refer to as RozumFormer. Our model is based on a bimodal (text-image) transformer architecture originally trained for solving tasks that use one or both modalities, such as language modeling, visual question answering, image captioning, text recognition, text-to-image generation, etc. The discussed model was adapted for robotic manipulation tasks by organizing the input sequence of tokens in a particular way, consisting of tokens for text, images, and actions. We demonstrated that such a model adapts well to new tasks and shows better results with fine-tuning than complete training in simulation and real environments. To transfer the model from the simulator to a real robot, new datasets were collected and annotated. In addition, experiments controlling the agent in a visual environment using reinforcement learning have shown that fine-tuning the model with a mixed dataset that includes examples from the initial visual-linguistic tasks only slightly decreases performance on these tasks, simplifying the addition of tasks from another domain.

   @article{Staroverov2023,
     dimensions = {true},
     title = {Fine-tuning Multimodal Transformer Models for Generating Actions in Virtual and Real Environments},
     volume = {11},
     url = {https://ieeexplore.ieee.org/document/10323309},
     doi = {10.1109/ACCESS.2023.3334791},
     pages = {130548--130559},
     journal = {{IEEE} Access},
     author = {Staroverov, Aleksei and Gorodetsky, Andrey S and Krishtopik, Andrei S and Yudin, Dmitry A and Kovalev, Alexey K and Panov, Aleksandr I},
     year = {2023},
     keywords = {mypub, scopus, frccsc, q1scopus, airi, mipt\_other, 70-2021-00138}
   }

2. AAAI

   TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformers

   Daniil Kirilenko, Anton Andreychuk, Aleksandr Panov, and 1 more author

   In Proceedings of the AAAI Conference on Artificial Intelligence, 2023

   Abs Bib PDF

   Heuristic search algorithms, e.g. A*, are the commonly used tools for pathfinding on grids, i.e. graphs of regular structure that are widely employed to represent environments in robotics, video games etc. Instance-independent heuristics for grid graphs, e.g. Manhattan distance, do not take the obstacles into account and, thus, the search led by such heuristics performs poorly in the obstacle-rich environments. To this end, we suggest learning the instance-dependent heuristic proxies that are supposed to notably increase the efficiency of the search. The first heuristic proxy we suggest to learn is the correction factor, i.e. the ratio between the instance independent cost-to-go estimate and the perfect one (computed offline at the training phase). Unlike learning the absolute values of the cost-to-go heuristic function, which was known before, when learning the correction factor the knowledge of the instance-independent heuristic is utilized. The second heuristic proxy is the path probability, which indicates how likely the grid cell is lying on the shortest path. This heuristic can be utilized in the Focal Search framework as the secondary heuristic, allowing us to preserve the guarantees on the bounded sub-optimality of the solution. We learn both suggested heuristics in a supervised fashion with the state-of-the-art neural networks containing attention blocks (transformers). We conduct a thorough empirical evaluation on a comprehensive dataset of planning tasks, showing that the suggested techniques i) reduce the computational effort of the A* up to a factor of \4\x while producing the solutions, which costs exceed the costs of the optimal solutions by less than \0.3\% on average; ii) outperform the competitors, which include the conventional techniques from the heuristic search, i.e. weighted A*, as well as the state-of-the-art learnable planners.

   @inproceedings{Kirilenko2023,
     dimensions = {true},
     title = {TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformers},
     volume = {37},
     url = {https://ojs.aaai.org/index.php/AAAI/article/view/26465},
     doi = {10.1609/aaai.v37i10.26465},
     pages = {12436--12443},
     booktitle = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
     author = {Kirilenko, Daniil and Andreychuk, Anton and Panov, Aleksandr and Yakovlev, Konstantin},
     year = {2023},
     keywords = {myconf, scopus, frccsc, airi, confastar}
   }

3. TNNLS

   When to Switch: Planning and Learning For Partially Observable Multi-Agent Pathfinding

   Alexey Skrynnik, Anton Andreychuk, Konstantin Yakovlev, and 1 more author

   IEEE Transactions on Neural Networks and Learning Systems, 2023

   Bib PDF

   @article{Skrynnik2023,
     dimensions = {true},
     title = {When to Switch: Planning and Learning For Partially Observable Multi-Agent Pathfinding},
     doi = {10.1109/TNNLS.2023.3303502},
     pages = {(In Press)},
     journal = {{IEEE} Transactions on Neural Networks and Learning Systems},
     shortjournal = {{TNNLS}},
     author = {Skrynnik, Alexey and Andreychuk, Anton and Yakovlev, Konstantin and Panov, Aleksandr},
     year = {2023},
     langid = {english},
     keywords = {appl, group1}
   }

4. RAL

   Policy Optimization to Learn Adaptive Motion Primitives in Path Planning With Dynamic Obstacles

   Brian Angulo, Aleksandr Panov, and Konstantin Yakovlev

   IEEE Robotics and Automation Letters, 2023

   Abs Bib PDF

   This paper addresses the kinodynamic motion planning for non-holonomic robots in dynamic environments with both static and dynamic obstacles – a challenging problem that lacks a universal solution yet. One of the promising approaches to solve it is decomposing the problem into the smaller sub-problems and combining the local solutions into the global one. The crux of any planning method for non- holonomic robots is the generation of motion primitives that generates solutions to local planning sub-problems. In this work we introduce a novel learnable steering function (policy), which takes into account kinodynamic constraints of the robot and both static and dynamic obstacles. This policy is efficiently trained via the policy optimization. Empirically, we show that our steering function generalizes well to unseen problems. We then plug in the trained policy into the sampling-based and lattice-based planners, and evaluate the resultant POLAMP algorithm (Policy Optimization that Learns Adaptive Motion Primitives) in a range of challenging setups that involve a car-like robot operating in the obstacle-rich parking-lot en- vironments. We show that POLAMP is able to plan collision- free kinodynamic trajectories with success rates higher than 92%, when 50 simultaneously moving obstacles populate the environment showing better performance than the state-of-the- art competitors.

   @article{Angulo2023,
     dimensions = {true},
     title = {Policy Optimization to Learn Adaptive Motion Primitives in Path Planning With Dynamic Obstacles},
     volume = {8},
     issn = {2377-3766},
     url = {https://ieeexplore.ieee.org/document/10003648/},
     doi = {10.1109/LRA.2022.3233261},
     pages = {824--831},
     number = {2},
     journal = {{IEEE} Robotics and Automation Letters},
     author = {Angulo, Brian and Panov, Aleksandr and Yakovlev, Konstantin},
     year = {2023},
     eprint = {2212.14307},
     keywords = {robotics, group1}
   }

5. Robotics

   Skill Fusion in Hybrid Robotic Framework for Visual Object Goal Navigation

   Aleksei Staroverov, Kirill Muravyev, Konstantin Yakovlev, and 1 more author

   Robotics, 2023

   Abs Bib HTML PDF

   In recent years, Embodied AI has become one of the main topics in robotics. For the agent to operate in human-centric environments, it needs the ability to explore previously unseen areas and to navigate to objects that humans want the agent to interact with. This task, which can be formulated as ObjectGoal Navigation (ObjectNav), is the main focus of this work. To solve this challenging problem, we suggest a hybrid framework consisting of both not-learnable and learnable modules and a switcher between them—SkillFusion. The former are more accurate, while the latter are more robust to sensors’ noise. To mitigate the sim-to-real gap, which often arises with learnable methods, we suggest training them in such a way that they are less environment-dependent. As a result, our method showed top results in both the Habitat simulator and during the evaluations on a real robot. Video and code for our approach can be found on our website: https://github.com/AIRI-Institute/skill-fusion (accessed on 13 July 2023).

   @article{Staroverov2024,
     dimensions = {true},
     title = {Skill {Fusion} in {Hybrid} {Robotic} {Framework} for {Visual} {Object} {Goal} {Navigation}},
     volume = {12},
     issn = {2218-6581},
     url = {https://www.mdpi.com/2218-6581/12/4/104},
     doi = {10.3390/robotics12040104},
     journal = {Robotics},
     author = {Staroverov, Aleksei and Muravyev, Kirill and Yakovlev, Konstantin and Panov, Aleksandr I},
     year = {2023}
   }

2022

1. CSR

   Vector Semiotic Model for Visual Question Answering

   Alexey K. Kovalev, Makhmud Shaban, Evgeny Osipov, and 1 more author

   Cognitive Systems Research, 2022

   Abs Bib PDF

   In this paper, we propose a Vector Semiotic Model as a possible solution to the symbol grounding problem in the context of Visual Question Answering. The Vector Semiotic Model combines the advantages of a Semiotic Approach implemented in the Sign-Based World Model and Vector Symbolic Architectures. The Sign-Based World Model represents information about a scene depicted on an input image in a structured way and grounds abstract objects in an agent’s sensory input. We use the Vector Symbolic Architecture to represent the elements of the Sign-Based World Model on a computational level. Properties of a high-dimensional space and operations defined for high-dimensional vectors allow encoding the whole scene into a high-dimensional vector with the preservation of the structure. That leads to the ability to apply explainable reasoning to answer an input question. We conducted experiments are on a CLEVR dataset and show results comparable to the state of the art. The proposed combination of approaches, first, leads to the possible solution of the symbol-grounding problem and, second, allows expanding current results to other intelligent tasks (collaborative robotics, embodied intellectual assistance, etc.).

   @article{Kovalev2021,
     dimensions = {true},
     title = {Vector Semiotic Model for Visual Question Answering},
     volume = {71},
     issn = {1389-0417},
     url = {https://www.sciencedirect.com/science/article/abs/pii/S1389041721000632},
     doi = {10.1016/j.cogsys.2021.09.001},
     pages = {52--63},
     journal = {Cognitive Systems Research},
     author = {Kovalev, Alexey K. and Shaban, Makhmud and Osipov, Evgeny and Panov, Aleksandr I.},
     year = {2022},
     keywords = {elibrary, mypub, scopus, wos, frccsc, computation, mipt, wos\_core, q1scopus, visual question answering, 20-71-10116, semiotic approach, causal network, grounding problem, preprint submitted to cognitive, q1wosprelim, symbol, vector-symbolic architecture}
   }

2. BrainInf

   Hierarchical intrinsically motivated agent planning behavior with dreaming in grid environments

   Evgenii Dzhivelikian, Artem Latyshev, Petr Kuderov, and 1 more author

   Brain Informatics, 2022

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   Biologically plausible models of learning may provide a crucial insight for building autonomous intelligent agents capable of performing a wide range of tasks. In this work, we propose a hierarchical model of an agent operating in an unfamiliar environment driven by a reinforcement signal. We use temporal memory to learn sparse distributed representation of state–actions and the basal ganglia model to learn effective action policy on different levels of abstraction. The learned model of the environment is utilized to generate an intrinsic motivation signal, which drives the agent in the absence of the extrinsic signal, and through acting in imagination, which we call dreaming. We demonstrate that the proposed architecture enables an agent to effectively reach goals in grid environments.

   @article{Dzhivelikian2022a,
     dimensions = {true},
     title = {Hierarchical intrinsically motivated agent planning behavior with dreaming in grid environments},
     volume = {9},
     issn = {2198-4018},
     url = {https://braininformatics.springeropen.com/articles/10.1186/s40708-022-00156-6},
     doi = {10.1186/s40708-022-00156-6},
     pages = {8},
     number = {1},
     journal = {Brain Informatics},
     author = {Dzhivelikian, Evgenii and Latyshev, Artem and Kuderov, Petr and Panov, Aleksandr I},
     year = {2022},
     keywords = {slap, group1}
   }

3. Access

   Hierarchical Landmark Policy Optimization for Visual Indoor Navigation

   Aleksei Staroverov, and Aleksandr Panov

   IEEE Access, 2022

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   In this paper, we study the problem of visual indoor navigation to an object that is defined by its semantic category. Recent works have shown significant achievements in the end-to-end reinforcement learning approach and modular systems. However, both approaches need a big step forward to be robust and practically applicable. To solve the problem of insufficient exploration of the scenes and make exploration more semantically meaningful, we extend standard task formulation and give the agent easily accessible landmarks in the form of the room locations and those types. The availability of landmarks allows the agent to build a hierarchical policy structure and achieve a success rate of 63% on validation scenes in a photo- realistic Habitat simulator. In a hierarchy, a low level consists of separately trained RL skills and a high level deterministic policy, which decides which skill is needed at the moment. Also, in this paper, we show the possibility of transferring a trained policy to a real robot. After a bit of training on the reconstructed real scene, the robot shows up to 79% SPL when solving the task of navigating to an arbitrary object.

   @article{Staroverov2022,
     dimensions = {true},
     title = {Hierarchical Landmark Policy Optimization for Visual Indoor Navigation},
     volume = {10},
     issn = {2169-3536},
     url = {https://ieeexplore.ieee.org/document/9795006/},
     doi = {10.1109/ACCESS.2022.3182803},
     pages = {70447--70455},
     journal = {{IEEE} Access},
     author = {Staroverov, Aleksei and Panov, Aleksandr},
     year = {2022},
     keywords = {robotics, group1}
   }

2021

1. KBS

   Forgetful experience replay in hierarchical reinforcement learning from expert demonstrations

   Alexey Skrynnik, Aleksey Staroverov, Ermek Aitygulov, and 3 more authors

   Knowledge-Based Systems, 2021

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   Deep reinforcement learning (RL) shows impressive results in complex gaming and robotic environments. These results are commonly achieved at the expense of huge computational costs and require an incredible number of episodes of interactions between the agent and the environment. Hierarchical methods and expert demonstrations are among the most promising approaches to improve the sample efficiency of reinforcement learning methods. In this paper, we propose a combination of methods that allow the agent to use low-quality demonstrations in complex vision-based environments with multiple related goals. Our Forgetful Experience Replay (ForgER) algorithm effectively handles expert data errors and reduces quality losses when adapting the action space and states representation to the agent’s capabilities. The proposed goal-oriented replay buffer structure allows the agent to automatically highlight sub-goals for solving complex hierarchical tasks in demonstrations. Our method has a high degree of versatility and can be integrated into various off-policy methods. The ForgER surpasses the existing state-of-the-art RL methods using expert demonstrations in complex environments. The solution based on our algorithm beats other solutions for the famous MineRL competition and allows the agent to demonstrate the behavior at the expert level.

   @article{Skrynnik2021,
     dimensions = {true},
     title = {Forgetful experience replay in hierarchical reinforcement learning from expert demonstrations},
     volume = {218},
     issn = {09507051},
     url = {https://linkinghub.elsevier.com/retrieve/pii/S0950705121001076},
     doi = {10.1016/j.knosys.2021.106844},
     pages = {106844},
     journal = {Knowledge-Based Systems},
     author = {Skrynnik, Alexey and Staroverov, Aleksey and Aitygulov, Ermek and Aksenov, Kirill and Davydov, Vasilii and Panov, Aleksandr I.},
     year = {2021},
     keywords = {slap, group1}
   }

2. CSR

   Hierarchical Deep Q-Network from imperfect demonstrations in Minecraft

   Alexey Skrynnik, Aleksey Staroverov, Ermek Aitygulov, and 3 more authors

   Cognitive Systems Research, 2021

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   We present hierarchical Deep Q-Network (HDQfD) that took first place in MineRL competition. HDQfD works on imperfect demonstrations utilize hierarchical structure of expert trajectories extracting effective sequence of meta-actions and subgoals. We introduce structured task dependent replay buffer and adaptive prioritizing technique that allow the HDQfD agent to gradually erase poor-quality expert data from the buffer. In this paper we present the details of the HDQfD algorithm and give the experimental results in Minecraft domain.

   @article{Skrynnik2021a,
     dimensions = {true},
     title = {Hierarchical Deep Q-Network from imperfect demonstrations in Minecraft},
     volume = {65},
     issn = {13890417},
     url = {https://www.sciencedirect.com/science/article/pii/S1389041720300723?via%3Dihub},
     doi = {10.1016/j.cogsys.2020.08.012},
     pages = {74--78},
     journal = {Cognitive Systems Research},
     author = {Skrynnik, Alexey and Staroverov, Aleksey and Aitygulov, Ermek and Aksenov, Kirill and Davydov, Vasilii and Panov, Aleksandr I.},
     year = {2021},
     eprint = {1912.08664v2},
     keywords = {slap, group1}
   }

3. Access

   Hybrid Policy Learning for Multi-Agent Pathfinding

   Alexey Skrynnik, Alexandra Yakovleva, Vasilii Davydov, and 2 more authors

   IEEE Access, 2021

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   In this work we study the behavior of groups of autonomous vehicles, which are the part of the Internet ofVehicles systems. One of the challenging modes of operation of such systems is the case when the observability of each vehicle is limited and the global/local communication is unstable, e.g. in the crowded parking lots. In such scenarios the vehicles have to rely on the local observations and exhibit cooperative behavior to ensure safe and efficient trips. This type of problems can be abstracted to the so-called multi- agent pathfinding when a group of agents, confined to a graph, have to find collision-free paths to their goals (ideally, minimizing an objective function e.g. travel time). Widely used algorithms for solving this problem rely on the assumption that a central controller exists for which the full state of the environment (i.e. the agents current positions, their targets, configuration of the static obstacles etc.) is known and they cannot be straightforwardly be adapted to the partially-observable setups. To this end, we suggest a novel approach which is based on the decomposition of the problem into the two sub-tasks: reaching the goal and avoiding the collisions. To accomplish each of this task we utilize reinforcement learning methods such as Deep Monte Carlo Tree Search, Q-mixing networks, and policy gradients methods to design the policies that map the agents’ observations to actions. Next, we introduce the policy-mixing mechanism to end up with a single hybrid policy that allows each agent to exhibit both types of behavior – the individual one (reaching the goal) and the cooperative one (avoiding the collisions with other agents). We conduct an extensive empirical evaluation that shows that the suggested hybrid-policy outperforms standalone stat-of-the-art reinforcement learning methods for this kind of problems by a notable margin.

   @article{Skrynnik2021b,
     dimensions = {true},
     title = {Hybrid Policy Learning for Multi-Agent Pathfinding},
     volume = {9},
     issn = {2169-3536},
     url = {https://ieeexplore.ieee.org/document/9532001/},
     doi = {10.1109/ACCESS.2021.3111321},
     pages = {126034--126047},
     journal = {{IEEE} Access},
     author = {Skrynnik, Alexey and Yakovleva, Alexandra and Davydov, Vasilii and Yakovlev, Konstantin and Panov, Aleksandr I.},
     year = {2021},
     keywords = {appl, group1}
   }

2020

1. Access

   Real-Time Object Navigation with Deep Neural Networks and Hierarchical Reinforcement Learning

   Aleksey Staroverov, Dmitry A. Yudin, Ilya Belkin, and 3 more authors

   IEEE Access, 2020

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   In the last years, deep learning and reinforcement learning methods have significantly improved mobile robots in such fields as perception, navigation, and planning. But there are still gaps in applying these methods to real robots due to the low computational efficiency of recent neural network architectures and their poor adaptability to robotic experiments’ realities. In this paper, we consider an important task in mobile robotics - navigation to an object using an RGB-D camera.We develop a new neural network framework for robot control that is fast and resistant to possible noise in sensors and actuators. We propose an original integration of semantic segmentation, mapping, localization, and reinforcement learning methods to improve the effectiveness of exploring the environment, finding the desired object, and quickly navigating to it. We created a new HISNav dataset based on the Habitat virtual environment, which allowed us to use simulation experiments to pre-train the model and then upload it to a real robot. Our architecture is adapted to work in a real-time environment and fully implements modern trends in this area.

   @article{Staroverov2020b,
     dimensions = {true},
     title = {Real-Time Object Navigation with Deep Neural Networks and Hierarchical Reinforcement Learning},
     volume = {8},
     issn = {2169-3536},
     url = {https://ieeexplore.ieee.org/document/9241850/},
     doi = {10.1109/ACCESS.2020.3034524},
     pages = {195608--195621},
     journal = {{IEEE} Access},
     author = {Staroverov, Aleksey and Yudin, Dmitry A. and Belkin, Ilya and Adeshkin, Vasily and Solomentsev, Yaroslav K. and Panov, Aleksandr I.},
     year = {2020},
     keywords = {robotics, group1}
   }

2016

1. CSR

   Multilayer cognitive architecture for UAV control

   Stanislav Emel’yanov, Dmitry Makarov, Aleksandr I. Panov, and 1 more author

   Cognitive Systems Research, 2016

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   Extensive use of unmanned aerial vehicles (UAVs) in recent years has induced the rapid growth of research areas related to UAV production. Among these, the design of control systems capable of automating a wide range of UAV activities is one of the most actively explored and evolving. Currently, researchers and developers are interested in designing control systems that can be referred to as intelligent, e.g. the systems which are suited to solve such tasks as planning, goal prioritization, coalition formation etc. and thus guarantee high levels of UAV autonomy. One of the principal problems in intelligent control system design is tying together various methods and models traditionally used in robotics and aimed at solving such tasks as dynamics modelling, control signal genera- tion, location and mapping, path planning etc. with the methods of behaviour modelling and planning which are thoroughly studied in cognitive science. Our work is aimed at solving this problem. We propose layered architecture — STRL (strategic, tactical, reactive, layered) — of the control system that au- tomates the behaviour generation using a cognitive approach while taking into account complex dynamics and kinematics of the control object (UAV).We use a special type of knowledge representation — sign world model — that is based on the psychological activity theory to describe individual behaviour planning and coalition formation processes. We also propose path planning methodology which serves as the mediator between the high-level cognitive activities and the reactive control signals generation. To generate these signals we use a state-dependent Riccati equation and specific method for solving it. We believe that utilization of the proposed architecture will broaden the spectrum of tasks which can be solved by the UAV’s coalition automatically, as well as raise the autonomy level of each individual member of that coalition.

   @article{Emelyanov2016,
     dimensions = {true},
     title = {Multilayer cognitive architecture for {UAV} control},
     volume = {39},
     issn = {1389-0417},
     url = {http://linkinghub.elsevier.com/retrieve/pii/S1389041716000048},
     doi = {10.1016/j.cogsys.2015.12.008},
     pages = {58--72},
     journal = {Cognitive Systems Research},
     author = {Emel’yanov, Stanislav and Makarov, Dmitry and Panov, Aleksandr I. and Yakovlev, Konstantin},
     year = {2016},
     keywords = {strl, group1}
   }