Co-robots Dynamics Human robot interaction Learning Legible paths Manipulation Manipulator Multi-Robot Planning Pose Estimation Rearrangement Robot Perception simulation Sociotechnological Systems tensegrity Topology
2023 |
Vieira, E R; Sivaramakrishnan, A; Song, Y; Granados, E; Gameiro, M; Mischaikow, K; Hung, Y; Bekris, K E 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. Abstract | Links | BibTeX | Tags: Dynamics, Topology @inproceedings{Vieira23_icra, title = {Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees}, author = {E. R. Vieira and A. Sivaramakrishnan and Y. Song and E. Granados and M. Gameiro and K. Mischaikow and Y. Hung and K. E. Bekris}, url = {https://arxiv.org/abs/2210.01292}, year = {2023}, date = {2023-05-23}, 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 = {Dynamics, Topology}, 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 E Morse Graphs: Topological Tools for Analyzing the Global Dynamics of Robot Controllers Inproceedings Workshop on the Algorithmic Foundations of Robotics (WAFR), 2022. Abstract | Links | BibTeX | Tags: Dynamics @inproceedings{morsegraphs_wafr22, 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 E Bekris}, url = {https://arxiv.org/abs/2202.08383}, year = {2022}, date = {2022-06-23}, 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 = {Dynamics}, 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. |
McMahon, T; Sivaramakrishnan, A; Kedia, K; Granados, E; Bekris, K E Terrain-Aware Learned Controllers for Sampling-Based Kinodynamic Planning over Physically Simulated Terrains Inproceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. BibTeX | Tags: Dynamics, Learning, Planning @inproceedings{terrain_sampling_simulated, title = {Terrain-Aware Learned Controllers for Sampling-Based Kinodynamic Planning over Physically Simulated Terrains}, author = {T McMahon and A Sivaramakrishnan and K Kedia and E Granados and K E Bekris }, year = {2022}, date = {2022-06-01}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, keywords = {Dynamics, Learning, Planning}, pubstate = {published}, tppubtype = {inproceedings} } |
Wang, K; Aanjaneya, M; Bekris, K E A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data Inproceedings IEEE International Conference on Robotics and Automation (ICRA), 2022. Abstract | Links | BibTeX | Tags: Dynamics, tensegrity @inproceedings{diff_engine_tensegrity_low_freq, title = {A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data}, author = {K Wang and M Aanjaneya and K E Bekris}, url = {https://arxiv.org/abs/2203.00041}, year = {2022}, date = {2022-05-24}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, abstract = {Tensegrity robots, composed of rigid rods and flexible cables, are difficult to accurately model and control given the presence of complex dynamics and high number of DoFs. Differentiable physics engines have been recently proposed as a data-driven approach for model identification of such complex robotic systems. These engines are often executed at a high-frequency to achieve accurate simulation. Ground truth trajectories for training differentiable engines, however, are not typically available at such high frequencies due to limitations of real-world sensors. The present work focuses on this frequency mismatch, which impacts the modeling accuracy. We proposed a recurrent structure for a differentiable physics engine of tensegrity robots, which can be trained effectively even with low-frequency trajectories. To train this new recurrent engine in a robust way, this work introduces relative to prior work: (i) a new implicit integration scheme, (ii) a progressive training pipeline, and (iii) a differentiable collision checker. A model of NASA's icosahedron SUPERballBot on MuJoCo is used as the ground truth system to collect training data. Simulated experiments show that once the recurrent differentiable engine has been trained given the low-frequency trajectories from MuJoCo, it is able to match the behavior of MuJoCo's system. The criterion for success is whether a locomotion strategy learned using the differentiable engine can be transferred back to the ground-truth system and result in a similar motion. Notably, the amount of ground truth data needed to train the differentiable engine, such that the policy is transferable to the ground truth system, is 1% of the data needed to train the policy directly on the ground-truth system. }, keywords = {Dynamics, tensegrity}, pubstate = {published}, tppubtype = {inproceedings} } Tensegrity robots, composed of rigid rods and flexible cables, are difficult to accurately model and control given the presence of complex dynamics and high number of DoFs. Differentiable physics engines have been recently proposed as a data-driven approach for model identification of such complex robotic systems. These engines are often executed at a high-frequency to achieve accurate simulation. Ground truth trajectories for training differentiable engines, however, are not typically available at such high frequencies due to limitations of real-world sensors. The present work focuses on this frequency mismatch, which impacts the modeling accuracy. We proposed a recurrent structure for a differentiable physics engine of tensegrity robots, which can be trained effectively even with low-frequency trajectories. To train this new recurrent engine in a robust way, this work introduces relative to prior work: (i) a new implicit integration scheme, (ii) a progressive training pipeline, and (iii) a differentiable collision checker. A model of NASA's icosahedron SUPERballBot on MuJoCo is used as the ground truth system to collect training data. Simulated experiments show that once the recurrent differentiable engine has been trained given the low-frequency trajectories from MuJoCo, it is able to match the behavior of MuJoCo's system. The criterion for success is whether a locomotion strategy learned using the differentiable engine can be transferred back to the ground-truth system and result in a similar motion. Notably, the amount of ground truth data needed to train the differentiable engine, such that the policy is transferable to the ground truth system, is 1% of the data needed to train the policy directly on the ground-truth system. |
Granados, E; Boularias, A; Bekris, K E; Aanjaneya, M Model Identification and Control of a Mobile Robot with Omnidirectional Wheels Using Differentiable Physics Inproceedings IEEE International Conference on Robotics and Automation (ICRA), 2022. Abstract | Links | BibTeX | Tags: Dynamics, Learning @inproceedings{model_identification_omnidirectional, title = {Model Identification and Control of a Mobile Robot with Omnidirectional Wheels Using Differentiable Physics}, author = {E Granados and A Boularias and K E Bekris and M Aanjaneya}, url = {https://orionquest.github.io/papers/MICLCMR/paper.html}, year = {2022}, date = {2022-05-24}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, abstract = {We present a new data-driven technique for predicting the motion of a low-cost omnidirectional mobile robot under the influence of motor torques and friction forces. Our method utilizes a novel differentiable physics engine for analytically computing the gradient of the deviation between predicted motion trajectories and real-world trajectories. This allows to automatically learn and fine-tune the unknown friction coefficients on-the-fly, by minimizing a carefully designed loss function using gradient descent. Experiments show that the predicted trajectories are in excellent agreement with their real-world counterparts. Our proposed approach is computationally superior to existing black-box optimization methods, requiring very few real-world samples for accurate trajectory prediction compared to physics-agnostic techniques, such as neural networks. Experiments also demonstrate that the proposed method allows the robot to quickly adapt to changes in the terrain. Our proposed approach combines the data-efficiency of classical analytical models that are derived from first principles, with the flexibility of data-driven methods, which makes it appropriate for low-cost mobile robots. }, keywords = {Dynamics, Learning}, pubstate = {published}, tppubtype = {inproceedings} } We present a new data-driven technique for predicting the motion of a low-cost omnidirectional mobile robot under the influence of motor torques and friction forces. Our method utilizes a novel differentiable physics engine for analytically computing the gradient of the deviation between predicted motion trajectories and real-world trajectories. This allows to automatically learn and fine-tune the unknown friction coefficients on-the-fly, by minimizing a carefully designed loss function using gradient descent. Experiments show that the predicted trajectories are in excellent agreement with their real-world counterparts. Our proposed approach is computationally superior to existing black-box optimization methods, requiring very few real-world samples for accurate trajectory prediction compared to physics-agnostic techniques, such as neural networks. Experiments also demonstrate that the proposed method allows the robot to quickly adapt to changes in the terrain. Our proposed approach combines the data-efficiency of classical analytical models that are derived from first principles, with the flexibility of data-driven methods, which makes it appropriate for low-cost mobile robots. |