I am a Post-Doctoral Associate in the Department of Mathematics in the School of Arts and Sciences atΒ Rutgers University). My primary research interest is topological data analysis applied to robotic controllers and dynamical systems.
Personal Website:Β https://ewerton-vieira.github.io/
Publications:
2024 |
Vieira, E; Sivaramakrishnan, A; Tangirala, S; Granados, E; Mischaikow, K; Bekris, K MORALS: Analysis of High-Dimensional Robot Controllers Via Topological Tools in a Latent Space Conference IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan (Nominated for Best Paper Award in Automation), 2024. @conference{Vieira:2024aa, title = {MORALS: Analysis of High-Dimensional Robot Controllers Via Topological Tools in a Latent Space}, author = {E Vieira and A Sivaramakrishnan and S Tangirala and E Granados and K Mischaikow and K Bekris}, url = {https://arxiv.org/abs/2310.03246}, year = {2024}, date = {2024-05-01}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, address = {Yokohama, Japan (Nominated for Best Paper Award in Automation)}, abstract = {Estimating the region of attraction (πππ°) for a robotic system's controller is essential for safe application and controller composition. Many existing methods require access to a closed-form expression that limit applicability to data-driven controllers. Methods that operate only over trajectory rollouts tend to be data-hungry. In prior work, we have demonstrated that topological tools based on Morse Graphs offer data-efficient πππ° estimation without needing an analytical model. They struggle, however, with high-dimensional systems as they operate over a discretization of the state space. This paper presents Morse Graph-aided discovery of Regions of Attraction in a learned Latent Space (πΌπΎππ°π»π). The approach combines autoencoding neural networks with Morse Graphs. πΌπΎππ°π»π shows promising predictive capabilities in estimating attractors and their πππ°s for data-driven controllers operating over high-dimensional systems, including a 67-dim humanoid robot and a 96-dim 3-fingered manipulator. It first projects the dynamics of the controlled system into a learned latent space. Then, it constructs a reduced form of Morse Graphs representing the bistability of the underlying dynamics, i.e., detecting when the controller results in a desired versus an undesired behavior. The evaluation on high-dimensional robotic datasets indicates the data efficiency of the approach in πππ° estimation.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Estimating the region of attraction (πππ°) for a robotic system's controller is essential for safe application and controller composition. Many existing methods require access to a closed-form expression that limit applicability to data-driven controllers. Methods that operate only over trajectory rollouts tend to be data-hungry. In prior work, we have demonstrated that topological tools based on Morse Graphs offer data-efficient πππ° estimation without needing an analytical model. They struggle, however, with high-dimensional systems as they operate over a discretization of the state space. This paper presents Morse Graph-aided discovery of Regions of Attraction in a learned Latent Space (πΌπΎππ°π»π). The approach combines autoencoding neural networks with Morse Graphs. πΌπΎππ°π»π shows promising predictive capabilities in estimating attractors and their πππ°s for data-driven controllers operating over high-dimensional systems, including a 67-dim humanoid robot and a 96-dim 3-fingered manipulator. It first projects the dynamics of the controlled system into a learned latent space. Then, it constructs a reduced form of Morse Graphs representing the bistability of the underlying dynamics, i.e., detecting when the controller results in a desired versus an undesired behavior. The evaluation on high-dimensional robotic datasets indicates the data efficiency of the approach in πππ° estimation. |
2023 |
Vieira, E; Gao, K; Nakhimovich, D; Bekris, K; Yu, J Persistent Homology Guided Monte-Carlo Tree Search for Effective Non-Prehensile Manipulation Inproceedings International Symposium on Experimental Robotics (ISER), 2023. @inproceedings{Vieira:2023ab, title = {Persistent Homology Guided Monte-Carlo Tree Search for Effective Non-Prehensile Manipulation}, author = {E Vieira and K Gao and D Nakhimovich and K Bekris and J Yu}, url = {http://arxiv.org/abs/2210.01283}, year = {2023}, date = {2023-12-01}, booktitle = {International Symposium on Experimental Robotics (ISER)}, abstract = {Performing object retrieval in real-world workspaces must tackle challenges including uncertainty and clutter. One option is to apply prehensile operations, which can be time consuming in highly-cluttered scenarios. On the other hand, non-prehensile actions, such as pushing simultaneously multiple objects, can help to quickly clear a cluttered workspace and retrieve a target object. Such actions, however, can also lead to increased uncertainty as it is difficult to estimate the outcome of pushing operations. The proposed framework in this work integrates topological tools and Monte-Carlo Tree Search (MCTS) to achieve effective and robust pushing for object retrieval. It employs persistent homology to automatically identify manageable clusters of blocking objects without the need for manually adjusting hyper-parameters. Then, MCTS uses this information to explore feasible actions to push groups of objects, aiming to minimize the number of operations needed to clear the path to the target. Real-world experiments using a Baxter robot, which involves some noise in actuation, show that the proposed framework achieves a higher success rate in solving retrieval tasks in dense clutter than alternatives. Moreover, it produces solutions with few pushing actions improving the overall execution time. More critically, it is robust enough that it allows one to plan the sequence of actions offline and then execute them reliably on a Baxter robot.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Performing object retrieval in real-world workspaces must tackle challenges including uncertainty and clutter. One option is to apply prehensile operations, which can be time consuming in highly-cluttered scenarios. On the other hand, non-prehensile actions, such as pushing simultaneously multiple objects, can help to quickly clear a cluttered workspace and retrieve a target object. Such actions, however, can also lead to increased uncertainty as it is difficult to estimate the outcome of pushing operations. The proposed framework in this work integrates topological tools and Monte-Carlo Tree Search (MCTS) to achieve effective and robust pushing for object retrieval. It employs persistent homology to automatically identify manageable clusters of blocking objects without the need for manually adjusting hyper-parameters. Then, MCTS uses this information to explore feasible actions to push groups of objects, aiming to minimize the number of operations needed to clear the path to the target. Real-world experiments using a Baxter robot, which involves some noise in actuation, show that the proposed framework achieves a higher success rate in solving retrieval tasks in dense clutter than alternatives. Moreover, it produces solutions with few pushing actions improving the overall execution time. More critically, it is robust enough that it allows one to plan the sequence of actions offline and then execute them reliably on a Baxter robot. |
Vieira, E; Sivaramakrishnan, A; Song, Y; Granados, E; Gameiro, M; Mischaikow, K; Hung, Y; Bekris, K Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees Inproceedings IEEE International Conference on Robotics and Automation (ICRA), London, UK, 2023. @inproceedings{Vieira:2023aa, title = {Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees}, author = {E Vieira and A Sivaramakrishnan and Y Song and E Granados and M Gameiro and K Mischaikow and Y Hung and K Bekris}, year = {2023}, date = {2023-05-01}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, address = {London, UK}, abstract = {This paper proposes an integration of surrogate modeling and topology to significantly reduce the amount of data required to describe the underlying global dynamics of robot controllers, including closed-box ones. A Gaussian Process (GP), trained with randomized short trajectories over the state-space, acts as a surrogate model for the underlying dynamical system. Then, a combinatorial representation is built and used to describe the dynamics in the form of a directed acyclic graph, known as it Morse graph. The Morse graph is able to describe the system's attractors and their corresponding regions of attraction (roa). Furthermore, a pointwise confidence level of the global dynamics estimation over the entire state space is provided. In contrast to alternatives, the framework does not require estimation of Lyapunov functions, alleviating the need for high prediction accuracy of the GP. The framework is suitable for data-driven controllers that do not expose an analytical model as long as Lipschitz-continuity is satisfied. The method is compared against established analytical and recent machine learning alternatives for estimating roa s, outperforming them in data efficiency without sacrificing accuracy.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper proposes an integration of surrogate modeling and topology to significantly reduce the amount of data required to describe the underlying global dynamics of robot controllers, including closed-box ones. A Gaussian Process (GP), trained with randomized short trajectories over the state-space, acts as a surrogate model for the underlying dynamical system. Then, a combinatorial representation is built and used to describe the dynamics in the form of a directed acyclic graph, known as it Morse graph. The Morse graph is able to describe the system's attractors and their corresponding regions of attraction (roa). Furthermore, a pointwise confidence level of the global dynamics estimation over the entire state space is provided. In contrast to alternatives, the framework does not require estimation of Lyapunov functions, alleviating the need for high prediction accuracy of the GP. The framework is suitable for data-driven controllers that do not expose an analytical model as long as Lipschitz-continuity is satisfied. The method is compared against established analytical and recent machine learning alternatives for estimating roa s, outperforming them in data efficiency without sacrificing accuracy. |
2022 |
Vieira, E; Granados, E; Sivaramakrishnan, A; Gameiro, M; Mischaikow, K; Bekris, K Morse Graphs: Topological Tools for Analyzing the Global Dynamics of Robot Controllers Inproceedings Workshop on the Algorithmic Foundations of Robotics (WAFR), 2022. @inproceedings{Vieira:2022aa, title = {Morse Graphs: Topological Tools for Analyzing the Global Dynamics of Robot Controllers}, author = {E Vieira and E Granados and A Sivaramakrishnan and M Gameiro and K Mischaikow and K Bekris}, url = {https://arxiv.org/abs/2202.08383}, year = {2022}, date = {2022-06-01}, booktitle = {Workshop on the Algorithmic Foundations of Robotics (WAFR)}, abstract = {Understanding the global dynamics of a robot controller, such as identifying attractors and their regions of attraction (RoA), is important for safe deployment and synthesizing more effective hybrid controllers. This paper proposes a topological framework to analyze the global dynamics of robot controllers, even data-driven ones, in an effective and explainable way. It builds a combinatorial representation representing the underlying system's state space and non-linear dynamics, which is summarized in a directed acyclic graph, the Morse graph. The approach only probes the dynamics locally by forward propagating short trajectories over a state-space discretization, which needs to be a Lipschitz-continuous function. The framework is evaluated given either numerical or data-driven controllers for classical robotic benchmarks. It is compared against established analytical and recent machine learning alternatives for estimating the RoAs of such controllers. It is shown to outperform them in accuracy and efficiency. It also provides deeper insights as it describes the global dynamics up to the discretization's resolution. This allows to use the Morse graph to identify how to synthesize controllers to form improved hybrid solutions or how to identify the physical limitations of a robotic system.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Understanding the global dynamics of a robot controller, such as identifying attractors and their regions of attraction (RoA), is important for safe deployment and synthesizing more effective hybrid controllers. This paper proposes a topological framework to analyze the global dynamics of robot controllers, even data-driven ones, in an effective and explainable way. It builds a combinatorial representation representing the underlying system's state space and non-linear dynamics, which is summarized in a directed acyclic graph, the Morse graph. The approach only probes the dynamics locally by forward propagating short trajectories over a state-space discretization, which needs to be a Lipschitz-continuous function. The framework is evaluated given either numerical or data-driven controllers for classical robotic benchmarks. It is compared against established analytical and recent machine learning alternatives for estimating the RoAs of such controllers. It is shown to outperform them in accuracy and efficiency. It also provides deeper insights as it describes the global dynamics up to the discretization's resolution. This allows to use the Morse graph to identify how to synthesize controllers to form improved hybrid solutions or how to identify the physical limitations of a robotic system. |
Vieira, E; Nakhimovich, D; Gao, K; Wang, R; Yu, J; Bekris, K Persistent Homology for Effective Non-Prehensile Manipulation Inproceedings IEEE International Conference on Robotics and Automation (ICRA), 2022. @inproceedings{Vieira:2022ab, title = {Persistent Homology for Effective Non-Prehensile Manipulation}, author = {E Vieira and D Nakhimovich and K Gao and R Wang and J Yu and K Bekris}, url = {https://arxiv.org/abs/2202.02937}, year = {2022}, date = {2022-05-01}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, abstract = {This work explores the use of topological tools for achieving effective non-prehensile manipulation in cluttered, constrained workspaces. In particular, it proposes the use of persistent homology as a guiding principle in identifying the appropriate non-prehensile actions, such as pushing, to clean a cluttered space with a robotic arm so as to allow the retrieval of a target object. Persistent homology enables the automatic identification of connected components of blocking objects in the space without the need for manual input or tuning of parameters. The proposed algorithm uses this information to push groups of cylindrical objects together and aims to minimize the number of pushing actions needed to reach to the target. Simulated experiments in a physics engine using a model of the Baxter robot show that the proposed topology-driven solution is achieving significantly higher success rate in solving such constrained problems relatively to state-of-the-art alternatives from the literature. It manages to keep the number of pushing actions low, is computationally efficient and the resulting decisions and motion appear natural for effectively solving such tasks.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This work explores the use of topological tools for achieving effective non-prehensile manipulation in cluttered, constrained workspaces. In particular, it proposes the use of persistent homology as a guiding principle in identifying the appropriate non-prehensile actions, such as pushing, to clean a cluttered space with a robotic arm so as to allow the retrieval of a target object. Persistent homology enables the automatic identification of connected components of blocking objects in the space without the need for manual input or tuning of parameters. The proposed algorithm uses this information to push groups of cylindrical objects together and aims to minimize the number of pushing actions needed to reach to the target. Simulated experiments in a physics engine using a model of the Baxter robot show that the proposed topology-driven solution is achieving significantly higher success rate in solving such constrained problems relatively to state-of-the-art alternatives from the literature. It manages to keep the number of pushing actions low, is computationally efficient and the resulting decisions and motion appear natural for effectively solving such tasks. |