List of papers
12 papers are listed.
You can click on each title to display more information, including authors, url to pdf, abstract and bibtex.
2023: 2 Papers
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DisMech A Discrete Differential Geometry–based Physical Simulator for Soft Robots and Structures
Authors:
- Andrew Choi
- Ran Jing
- Andrew Sabelhaus
- Mohammad Khalid Jawed
Abstract:
Fast, accurate, and generalizable simulations are a key enabler of modern advances in robot design and control. However, existing simulation frameworks in robotics either model rigid environments and mechanisms only, or if they include flexible or soft structures, suffer significantly in one or more of these performance areas. To close this "sim2real" gap, we introduce DisMech, a simulation environment that models highly dynamic motions of rod-like soft continuum robots and structures, quickly and accurately, with arbitrary connections between them. Our methodology combines a fully implicit discrete differential geometry-based physics solver with fast and accurate contact handling, all in an intuitive software interface. Crucially, we propose a gradient descent approach to easily map the motions of hardware robot prototypes to control inputs in DisMech. We validate DisMech through several highly-nuanced soft robot simulations while demonstrating an order of magnitude speed increase over previous state of the art. Our real2sim validation shows high physical accuracy versus hardware, even with complicated soft actuation mechanisms such as shape memory alloy wires. With its low computational cost, physical accuracy, and ease of use, DisMech can accelerate translation of sim-based control for both soft robotics and deformable object manipulation.Links:
Bibtex:
@article{Choi_2024, title={DisMech: A Discrete Differential Geometry-Based Physical Simulator for Soft Robots and Structures}, volume={9}, ISSN={2377-3774}, url={http://dx.doi.org/10.1109/lra.2024.3365292}, DOI={10.1109/lra.2024.3365292}, number={4}, journal={IEEE Robotics and Automation Letters}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Choi, Andrew and Jing, Ran and Sabelhaus, Andrew P. and Jawed, Mohammad Khalid}, year={2024}, month=apr, pages={3483–3490} }
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SoftZoo A Soft Robot Co–design Benchmark For Locomotion In Diverse Environments
Authors:
- Tsun-Hsuan Wang
- Pingchuan Ma
- Andrew Everett Spielberg
- Zhou Xian
- Hao Zhang
- Joshua B. Tenenbaum
- Daniela Rus
- Chuang Gan
Abstract:
While significant research progress has been made in robot learning for control, unique challenges arise when simultaneously co-optimizing morphology. Existing work has typically been tailored for particular environments or representations. In order to more fully understand inherent design and performance tradeoffs and accelerate the development of new breeds of soft robots, a comprehensive virtual platform with well-established tasks, environments, and evaluation metrics is needed. In this work, we introduce SoftZoo, a soft robot co-design platform for locomotion in diverse environments. SoftZoo supports an extensive, naturally-inspired material set, including the ability to simulate environments such as flat ground, desert, wetland, clay, ice, snow, shallow water, and ocean. Further, it provides a variety of tasks relevant for soft robotics, including fast locomotion, agile turning, and path following, as well as differentiable design representations for morphology and control. Combined, these elements form a feature-rich platform for analysis and development of soft robot co-design algorithms. We benchmark prevalent representations and co-design algorithms, and shed light on 1) the interplay between environment, morphology, and behavior; 2) the importance of design space representations; 3) the ambiguity in muscle formation and controller synthesis; and 4) the value of differentiable physics. We envision that SoftZoo will serve as a standard platform and template an approach toward the development of novel representations and algorithms for co-designing soft robots' behavioral and morphological intelligence.Links:
Bibtex:
@article{Wang2023SoftZoo:, title={SoftZoo A Soft Robot Co–design Benchmark For Locomotion In Diverse Environments}, author={Wang, Tsun-Hsuan and Ma, Pingchuan and Everett Spielberg, Andrew and Xian, Zhou and Zhang, Hao and B. Tenenbaum, Joshua and Rus, Daniela and Gan, Chuang}, journal={arXiv preprint arXiv:2303.09555v1}, year={2023} }
2022: 3 Papers
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A Fully Implicit Method for Robust Frictional Contact Handling in Elastic Rods
Authors:
- Dezhong Tong
- Andrew Choi
- Jungseock Joo
- M. Khalid Jawed
Abstract:
Accurate frictional contact is critical in simulating the assembly of rod-like structures in the practical world, such as knots, hairs, flagella, and more. Due to their high geometric nonlinearity and elasticity, rod-on-rod contact remains a challenging problem tackled by researchers in both computational mechanics and computer graphics. Typically, frictional contact is regarded as constraints for the equations of motions of a system. Such constraints are often computed independently at every time step in a dynamic simulation, thus slowing down the simulation and possibly introducing numerical convergence issues. This paper proposes a fully implicit penalty-based frictional contact method, Implicit Contact Model (IMC), that efficiently and robustly captures accurate frictional contact responses. We showcase our algorithm's performance in achieving visually realistic results for the challenging and novel contact scenario of flagella bundling in fluid medium, a significant phenomenon in biology that motivates novel engineering applications in soft robotics. In addition to this, we offer a side-by-side comparison with Incremental Potential Contact (IPC), a state-of-the-art contact handling algorithm. We show that IMC possesses comparable performance to IPC while converging at a faster rate.Links:
Bibtex:
@article{Tong2022A, title={A Fully Implicit Method for Robust Frictional Contact Handling in Elastic Rods}, author={Tong, Dezhong and Choi, Andrew and Joo, Jungseock and Khalid Jawed, M.}, journal={arXiv preprint arXiv:2205.10309v3}, year={2022} }
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Co-evolving morphology and control of soft robots using a single genome
Authors:
- Fabio Tanaka
- Claus Aranha
Abstract:
When simulating soft robots, both their morphology and their controllers play important roles in task performance. This paper introduces a new method to co-evolve these two components in the same process. We do that by using the hyperNEAT algorithm to generate two separate neural networks in one pass, one responsible for the design of the robot body structure and the other for the control of the robot. The key difference between our method and most existing approaches is that it does not treat the development of the morphology and the controller as separate processes. Similar to nature, our method derives both the "brain" and the "body" of an agent from a single genome and develops them together. While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects "brain" and the "body" at the same time. Additionally, we present a new speciation function that takes into consideration both the genotypic distance, as is the standard for NEAT, and the similarity between robot bodies. By using this function, agents with very different bodies are more likely to be in different species, this allows robots with different morphologies to have more specialized controllers since they won't crossover with other robots that are too different from them. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster when compared to having separated genomes for body and control. The agents in our population also show morphologies with a high degree of regularity and controllers capable of coordinating the voxels to produce the necessary movements.Links:
Bibtex:
@inproceedings{Tanaka_2022, title={Co-evolving morphology and control of soft robots using a single genome}, url={http://dx.doi.org/10.1109/ssci51031.2022.10022230}, DOI={10.1109/ssci51031.2022.10022230}, booktitle={2022 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher={IEEE}, author={Tanaka, Fabio and Aranha, Claus}, year={2022}, month=dec }
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Evolution Gym A Large–Scale Benchmark for Evolving Soft Robots
Authors:
- Jagdeep Singh Bhatia
- Holly Jackson
- Yunsheng Tian
- Jie Xu
- Wojciech Matusik
Abstract:
Both the design and control of a robot play equally important roles in its task performance. However, while optimal control is well studied in the machine learning and robotics community, less attention is placed on finding the optimal robot design. This is mainly because co-optimizing design and control in robotics is characterized as a challenging problem, and more importantly, a comprehensive evaluation benchmark for co-optimization does not exist. In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots. In our benchmark, each robot is composed of different types of voxels (e.g., soft, rigid, actuators), resulting in a modular and expressive robot design space. Our benchmark environments span a wide range of tasks, including locomotion on various types of terrains and manipulation. Furthermore, we develop several robot co-evolution algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques. Evaluating the algorithms on our benchmark platform, we observe robots exhibiting increasingly complex behaviors as evolution progresses, with the best evolved designs solving many of our proposed tasks. Additionally, even though robot designs are evolved autonomously from scratch without prior knowledge, they often grow to resemble existing natural creatures while outperforming hand-designed robots. Nevertheless, all tested algorithms fail to find robots that succeed in our hardest environments. This suggests that more advanced algorithms are required to explore the high-dimensional design space and evolve increasingly intelligent robots -- an area of research in which we hope Evolution Gym will accelerate progress. Our website with code, environments, documentation, and tutorials is available at http://evogym.csail.mit.edu.Links:
Bibtex:
@article{Singh Bhatia2022Evolution, title={Evolution Gym A Large–Scale Benchmark for Evolving Soft Robots}, author={Singh Bhatia, Jagdeep and Jackson, Holly and Tian, Yunsheng and Xu, Jie and Matusik, Wojciech}, journal={arXiv preprint arXiv:2201.09863v1}, year={2022} }
2021: 2 Papers
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Implicit Contact Model for Discrete Elastic Rods in Knot Tying
Authors:
- Andrew Choi
- Dezhong Tong
- Mohammad K. Jawed
- Jungseock Joo
Links:
Bibtex:
@article{Choi_2021, title={Implicit Contact Model for Discrete Elastic Rods in Knot Tying}, volume={88}, ISSN={1528-9036}, url={http://dx.doi.org/10.1115/1.4050238}, DOI={10.1115/1.4050238}, number={5}, journal={Journal of Applied Mechanics}, publisher={ASME International}, author={Choi, Andrew and Tong, Dezhong and Jawed, Mohammad K. and Joo, Jungseock}, year={2021}, month=mar }
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SoMo Fast and Accurate Simulations of Continuum Robots in Complex Environments
Authors:
- Moritz A. Graule
- Clark B. Teeple
- Thomas P. McCarthy
- Grace R. Kim
- Randall C. St. Louis
- Robert J. Wood
Links:
Bibtex:
@inproceedings{Graule_2021, title={SoMo: Fast and Accurate Simulations of Continuum Robots in Complex Environments}, url={http://dx.doi.org/10.1109/iros51168.2021.9636059}, DOI={10.1109/iros51168.2021.9636059}, booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, publisher={IEEE}, author={Graule, Moritz A. and Teeple, Clark B. and McCarthy, Thomas P. and Kim, Grace R. and St. Louis, Randall C. and Wood, Robert J.}, year={2021}, month=sep, pages={3934–3941} }
2020: 4 Papers
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2D–VSR–Sim A simulation tool for the optimization of 2–D voxel–based soft robots
Authors:
- Eric Medvet
- Alberto Bartoli
- Andrea De Lorenzo
- Stefano Seriani
Links:
Bibtex:
@article{Medvet_2020, title={2D-VSR-Sim: A simulation tool for the optimization of 2-D voxel-based soft robots}, volume={12}, ISSN={2352-7110}, url={http://dx.doi.org/10.1016/j.softx.2020.100573}, DOI={10.1016/j.softx.2020.100573}, journal={SoftwareX}, publisher={Elsevier BV}, author={Medvet, Eric and Bartoli, Alberto and De Lorenzo, Andrea and Seriani, Stefano}, year={2020}, month=jul, pages={100573} }
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Design, Validation, and Case Studies of 2D-VSR-Sim, an Optimization-friendly Simulator of 2-D Voxel-based Soft Robots
Authors:
- Eric Medvet
- Alberto Bartoli
- Andrea De Lorenzo
- Stefano Seriani
Abstract:
Voxel-based soft robots (VSRs) are aggregations of soft blocks whose design is amenable to optimization. We here present a software, 2D-VSR-Sim, for facilitating research concerning the optimization of VSRs body and brain. The software, written in Java, provides consistent interfaces for all the VSRs aspects suitable for optimization and considers by design the presence of sensing, i.e., the possibility of exploiting the feedback from the environment for controlling the VSR. We experimentally characterize, from a mechanical point of view, the VSRs that can be simulated with 2D-VSR-Sim and we discuss the computational burden of the simulation. Finally, we show how 2D-VSR-Sim can be used to repeat the experiments of significant previous studies and, in perspective, to provide experimental answers to a variety of research questions.Links:
Bibtex:
@article{Medvet2020Design, title={Design, Validation, and Case Studies of 2D-VSR-Sim, an Optimization-friendly Simulator of 2-D Voxel-based Soft Robots}, author={Medvet, Eric and Bartoli, Alberto and De Lorenzo, Andrea and Seriani, Stefano}, journal={arXiv preprint arXiv:2001.08617v2}, year={2020} }
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Elastica A compliant mechanics environment for soft robotic control
Authors:
- Noel Naughton
- Jiarui Sun
- Arman Tekinalp
- Girish Chowdhary
- Mattia Gazzola
Abstract:
Soft robots are notoriously hard to control. This is partly due to the scarcity of models able to capture their complex continuum mechanics, resulting in a lack of control methodologies that take full advantage of body compliance. Currently available simulation methods are either too computational demanding or overly simplistic in their physical assumptions, leading to a paucity of available simulation resources for developing such control schemes. To address this, we introduce Elastica, a free, open-source simulation environment for soft, slender rods that can bend, twist, shear and stretch. We demonstrate how Elastica can be coupled with five state-of-the-art reinforcement learning algorithms to successfully control a soft, compliant robotic arm and complete increasingly challenging tasks.Links:
Bibtex:
@article{Naughton_2021, title={Elastica: A Compliant Mechanics Environment for Soft Robotic Control}, volume={6}, ISSN={2377-3774}, url={http://dx.doi.org/10.1109/lra.2021.3063698}, DOI={10.1109/lra.2021.3063698}, number={2}, journal={IEEE Robotics and Automation Letters}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Naughton, Noel and Sun, Jiarui and Tekinalp, Arman and Parthasarathy, Tejaswin and Chowdhary, Girish and Gazzola, Mattia}, year={2021}, month=apr, pages={3389–3396} }
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Truncated regular octahedral tensegrity-based mechanical metamaterial with tunable and programmable Poisson's ratio
Authors:
- Xu Yin
- Zhi-Ying Gao
- Shuai Zhang
- Li-Yuan Zhang
- Guang-Kui Xu
Links:
Bibtex:
@article{Yin_2020, title={Truncated regular octahedral tensegrity-based mechanical metamaterial with tunable and programmable Poisson’s ratio}, volume={167}, ISSN={0020-7403}, url={http://dx.doi.org/10.1016/j.ijmecsci.2019.105285}, DOI={10.1016/j.ijmecsci.2019.105285}, journal={International Journal of Mechanical Sciences}, publisher={Elsevier BV}, author={Yin, Xu and Gao, Zhi-Ying and Zhang, Shuai and Zhang, Li-Yuan and Xu, Guang-Kui}, year={2020}, month=feb, pages={105285} }
2015: 1 Papers
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Real-time control of soft-robots using asynchronous finite element modeling
Authors:
- Frederick Largilliere
- Valerian Verona
- Eulalie Coevoet
- Mario Sanz-Lopez
- Jeremie Dequidt
- Christian Duriez
Links:
Bibtex:
@inproceedings{Largilliere_2015, title={Real-time control of soft-robots using asynchronous finite element modeling}, url={http://dx.doi.org/10.1109/icra.2015.7139541}, DOI={10.1109/icra.2015.7139541}, booktitle={2015 IEEE International Conference on Robotics and Automation (ICRA)}, publisher={IEEE}, author={Largilliere, Frederick and Verona, Valerian and Coevoet, Eulalie and Sanz-Lopez, Mario and Dequidt, Jeremie and Duriez, Christian}, year={2015}, month=may }