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RobotDART: a versatile robot simulator for robotics and machine learning researchers

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RobotDART [![Build Status](https://github.com/resibots/robot_dart/actions/workflows/ci_linux.yml/badge.svg?branch=master)](https://github.com/resibots/robot_dart/actions/workflows/ci_linux.yml) [![Build Status Mac](https://github.com/resibots/robot_dart/actions/workflows/ci_mac.yml/badge.svg?branch=master)](https://github.com/resibots/robot_dart/actions/workflows/ci_mac.yml) [![Build Status Website](https://github.com/NOSALRO/robot_dart/actions/workflows/pages/pages-build-deployment/badge.svg?branch=master)](https://github.com/NOSALRO/robot_dart/actions/workflows/pages/pages-build-deployment)
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RobotDART is a C++ robot simulator (with optional Python bindings) built on top of the [DART] physics engine. The RobotDART simulator is intended to be used by Robotics and Machine Learning researchers who want to write controllers or test learning algorithms without the delays and overhead that usually comes with other simulators (e.g., Gazebo, Coppelia-sim).

## Documentation

Documentation is available at: [https://nosalro.github.io/robot_dart/](https://nosalro.github.io/robot_dart/)

## Authors

- Author/Maintainer: Konstantinos Chatzilygeroudis (University of Patras)
- Active contributors: Dionis Totsila (Inria and University of Patras), Jean-Baptiste Mouret (Inria)
- Other contributors: Antoine Cully, Vassilis Vassiliades, Vaios Papaspyros

## Citing RobotDART

If you use this code in a scientific publication, please use the following citation:

```bibtex
@article{chatzilygeroudis2024robot,
        title={{RobotDART: a versatile robot simulator for robotics and machine learning researchers}},
        author={Chatzilygeroudis, Konstantinos and Dionis, Totsila and Mouret, Jean-Baptiste},
        year={2024},
        booktitle={{Preprint (Submitted to JOSS)}}
      }
```

## Acknowledgments

This work was supported by the [Hellenic Foundation for Research and Innovation](https://www.elidek.gr/en/homepage/) (H.F.R.I.) under the "3rd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers" (Project Acronym: NOSALRO, Project Number: 7541).

This work was conducted within the [Laboratory of Automation and Robotics](https://lar.ece.upatras.gr/) (LAR), Department of Electrical and Computer Engineering, and the [Computational Intelligence Lab](http://cilab.math.upatras.gr/) (CILab), Department of Mathematics at the University of Patras, Greece.

<!-- This work was partly supported by the ResiBots ERC Project (http://www.resibots.eu).

## Contributing

Check out our [contribution guidelines](https://github.com/NOSALRO/robot_dart/tree/master/.github/CONTRIBUTING.md) to get started.

## License

[BSD 2-Clause "Simplified" License](https://opensource.org/license/bsd-2-clause/)

## Scientific Publications using RobotDART (indicative list, ordered by date)

1. Anne, T. and Mouret, J.B., 2024. Parametric-Task MAP-Elites. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). [(pdf)](https://arxiv.org/pdf/2402.01275)

1. Ivaldi, S. and Ghini, E., 2023, June. Teleoperating a robot for removing asbestos tiles on roofs: insights from a pilot study. In 2023 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO) (pp. 128-133). IEEE. [(pdf)](https://ieeexplore.ieee.org/iel7/10187190/10187410/10187476.pdf)

1. Khadivar, F., Chatzilygeroudis, K. and Billard, A., 2023. Self-correcting quadratic programming-based robot control. IEEE Transactions on Systems, Man, and Cybernetics: Systems. [(pdf)](https://infoscience.epfl.ch/record/301975/files/Self_Correcting_Quadratic_Programming_based_Robot_Control_IEEE_SMCA.pdf)

1. Souza, A.O., Grenier, J., Charpillet, F., Maurice, P. and Ivaldi, S., 2023, June. Towards data-driven predictive control of active upper-body exoskeletons for load carrying. In 2023 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO) (pp. 59-64). IEEE. [(pdf)](https://ieeexplore.ieee.org/iel7/10187190/10187410/10187548.pdf)

1. Chatzilygeroudis, K.I., Tsakonas, C.G. and Vrahatis, M.N., 2023, July. Evolving Dynamic Locomotion Policies in Minutes. In 2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-8). IEEE. [(pdf)](https://ieeexplore.ieee.org/iel7/10345839/10345840/10345937.pdf)

1. Tsakonas, C.G. and Chatzilygeroudis, K.I., 2023, July. Effective Skill Learning via Autonomous Goal Representation Learning. In 2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-8). IEEE. [(pdf)](https://ieeexplore.ieee.org/iel7/10345839/10345840/10345879.pdf)

1. Totsila, D., Chatzilygeroudis, K., Hadjivelichkov, D., Modugno, V., Hatzilygeroudis, I. and Kanoulas, D., 2023. End-to-End Stable Imitation Learning via Autonomous Neural Dynamic Policies. Life-Long Learning with Human Help (L3H2) Workshop, ICRA. [(pdf)](https://arxiv.org/pdf/2305.12886)

1. Allard, M., Smith, S.C., Chatzilygeroudis, K., Lim, B. and Cully, A., 2023. Online damage recovery for physical robots with hierarchical quality-diversity. ACM Transactions on Evolutionary Learning, 3(2), pp.1-23. [(pdf)](https://dl.acm.org/doi/pdf/10.1145/3596912)

1. Anne, T., Dalin, E., Bergonzani, I., Ivaldi, S. and Mouret, J.B., 2022. First do not fall: learning to exploit a wall with a damaged humanoid robot. IEEE Robotics and Automation Letters, 7(4), pp.9028-9035. [(pdf)](https://arxiv.org/pdf/2203.00316)

1. Mayr, M., Ahmad, F., Chatzilygeroudis, K., Nardi, L. and Krueger, V., 2022, December. Skill-based multi-objective reinforcement learning of industrial robot tasks with planning and knowledge integration. In 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 1995-2002). IEEE. [(pdf)](https://arxiv.org/pdf/2203.10033)

1. Grillotti, L. and Cully, A., 2022. Unsupervised behavior discovery with quality-diversity optimization. IEEE Transactions on Evolutionary Computation, 26(6), pp.1539-1552. [(pdf)](https://ieeexplore.ieee.org/iel7/4235/4358751/09736995.pdf)

1. Lim, B., Grillotti, L., Bernasconi, L. and Cully, A., 2022, May. Dynamics-aware quality-diversity for efficient learning of skill repertoires. In 2022 International Conference on Robotics and Automation (ICRA) (pp. 5360-5366). IEEE. [(pdf)](https://ieeexplore.ieee.org/iel7/9811522/9811357/09811559.pdf)

1. Tsinganos, K., Chatzilygeroudis, K., Hadjivelichkov, D., Komninos, T., Dermatas, E. and Kanoulas, D., 2022. Behavior policy learning: Learning multi-stage tasks via solution sketches and model-based controllers. Frontiers in Robotics and AI, 9, p.974537. [(pdf)](https://www.frontiersin.org/articles/10.3389/frobt.2022.974537/full)

1. d'Elia, E., Mouret, J.B., Kober, J. and Ivaldi, S., 2022, October. Automatic tuning and selection of whole-body controllers. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 12935-12941). IEEE. [(pdf)](https://ieeexplore.ieee.org/iel7/9981026/9981028/09981058.pdf)

1. Mayr, M., Hvarfner, C., Chatzilygeroudis, K., Nardi, L. and Krueger, V., 2022, August. Learning skill-based industrial robot tasks with user priors. In 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (pp. 1485-1492). IEEE. [(pdf)](https://ieeexplore.ieee.org/iel7/9926286/9926419/09926713.pdf)

1. Allard, M., Smith, S.C., Chatzilygeroudis, K. and Cully, A., 2022, July. Hierarchical quality-diversity for online damage recovery. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 58-67). [(pdf)](https://dl.acm.org/doi/pdf/10.1145/3512290.3528751)

1. Mayr, M., Ahmad, F., Chatzilygeroudis, K., Nardi, L. and Krueger, V., 2022. Combining planning, reasoning and reinforcement learning to solve industrial robot tasks. 2nd Workshop on Trends and Advances in Machine Learning and Automated Reasoning for Intelligent Robots and Systems, IROS. [(pdf)](https://arxiv.org/pdf/2212.03570)

1. Cully, A., 2021, June. Multi-emitter map-elites: improving quality, diversity and data efficiency with heterogeneous sets of emitters. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 84-92). [(pdf)](https://dl.acm.org/doi/pdf/10.1145/3449639.3459326)

1. Mayr, M., Chatzilygeroudis, K., Ahmad, F., Nardi, L. and Krueger, V., 2021, September. Learning of parameters in behavior trees for movement skills. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7572-7579). IEEE. [(pdf)](https://ieeexplore.ieee.org/iel7/9635848/9635849/09636292.pdf)

1. Penco, L., Hoffman, E.M., Modugno, V., Gomes, W., Mouret, J.B. and Ivaldi, S., 2020. Learning robust task priorities and gains for control of redundant robots. IEEE Robotics and Automation Letters, 5(2), pp.2626-2633. [(pdf)](https://inria.hal.science/hal-02456663/document)

1. Flageat, M. and Cully, A., 2020, July. Fast and stable MAP-Elites in noisy domains using deep grids. In Artificial Life Conference Proceedings 32 (pp. 273-282). One Rogers Street, Cambridge, MA 02142-1209, USA journals-info@ mit. edu: MIT Press. [(pdf)](https://direct.mit.edu/isal/proceedings-pdf/isal2020/32/273/1908606/isal_a_00316.pdf)

1. Paul, S., Chatzilygeroudis, K., Ciosek, K., Mouret, J.B., Osborne, M.A. and Whiteson, S., 2020. Robust reinforcement learning with Bayesian optimisation and quadrature. Journal of Machine Learning Research, 21(151), pp.1-31. [(pdf)](https://www.jmlr.org/papers/volume21/18-216/18-216.pdf)

1. Chatzilygeroudis, K., Vassiliades, V. and Mouret, J.B., 2018. Reset-free trial-and-error learning for robot damage recovery. Robotics and Autonomous Systems, 100, pp.236-250. [(pdf)](https://www.sciencedirect.com/science/article/pii/S0921889017302440)

1. Pautrat, R., Chatzilygeroudis, K. and Mouret, J.B., 2018, May. Bayesian optimization with automatic prior selection for data-efficient direct policy search. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 7571-7578). IEEE. [(pdf)](https://ieeexplore.ieee.org/iel7/8449910/8460178/08463197.pdf)

1. Chatzilygeroudis, K. and Mouret, J.B., 2018, May. Using parameterized black-box priors to scale up model-based policy search for robotics. In 2018 IEEE international conference on robotics and automation (ICRA) (pp. 5121-5128). IEEE. [(pdf)](https://ieeexplore.ieee.org/iel7/8449910/8460178/08461083.pdf)

1. Paul, S., Chatzilygeroudis, K., Ciosek, K., Mouret, J.B., Osborne, M. and Whiteson, S., 2018, April. Alternating optimisation and quadrature for robust control. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1). [(pdf)](https://ojs.aaai.org/index.php/AAAI/article/view/11687/11546)

1. Kaushik, R., Chatzilygeroudis, K. and Mouret, J.B., 2018, October. Multi-objective model-based policy search for data-efficient learning with sparse rewards. In Conference on Robot Learning (pp. 839-855). PMLR. [(pdf)](http://proceedings.mlr.press/v87/kaushik18a/kaushik18a.pdf)

1. Vassiliades, V., Chatzilygeroudis, K. and Mouret, J.B., 2017. Using centroidal voronoi tessellations to scale up the multidimensional archive of phenotypic elites algorithm. IEEE Transactions on Evolutionary Computation, 22(4), pp.623-630. [(pdf)](https://ieeexplore.ieee.org/iel7/4235/8423223/08000667.pdf)

1. Mouret, J.B. and Chatzilygeroudis, K., 2017, July. 20 years of reality gap: a few thoughts about simulators in evolutionary robotics. In Proceedings of the genetic and evolutionary computation conference companion (pp. 1121-1124). [(pdf)](https://dl.acm.org/doi/pdf/10.1145/3067695.3082052)

1. Papaspyros, V., Chatzilygeroudis, K., Vassiliades, V. and Mouret, J.B., 2016. Safety-aware robot damage recovery using constrained bayesian optimization and simulated priors. BayesOpt '16: Proceedings of the International Workshop "Bayesian Optimization: Black-box Optimization and Beyond", NeurIPS. [(pdf)](https://arxiv.org/pdf/1611.09419)

1. Chatzilygeroudis, K., Cully, A. and Mouret, J.B., 2016. Towards semi-episodic learning for robot damage recovery. AILTA '16: Proceedings of the International Workshop "AI for Long-term Autonomy", ICRA. [(pdf)](https://arxiv.org/pdf/1610.01407)

[DART]: http://dartsim.github.io/

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