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References

iLQG, iLEQG

Part of our iLQG and iLEQG implementations are based on the following papers:

  • E. Todorov and W. Li, "A generalized iterative lqg method for locally-optimal feedback control of constrained nonlinear stochastic systems," in Proceedings of the 2005, American Control Conference, 2005. IEEE, 2005, pp. 300–306.

  • Y. Tassa, T. Erez, and E. Todorov, "Synthesis and stabilization of complex behaviors through online trajectory optimization," in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2012, pp. 4906–4913.

  • J. van den Berg, S. Patil, and R. Alterovitz, "Motion planning under uncertainty using iterative local optimization in belief space," The International Journal of Robotics Research, vol. 31, no. 11, pp. 1263–1278, 2012.

  • M. Wang, N. Mehr, A. Gaidon, and M. Schwager, "Game-theoretic planning for risk-aware interactive agents," in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2020, pp.6998–7005.

RAT iLQR, RAT iLQR++

The RAT iLQR algorithm is originally presented in our paper:

  • H. Nishimura, N. Mehr, A. Gaidon, and M. Schwager, "Rat ilqr: a risk auto-tuning controller to optimally account for stochastic model mismatch," IEEE Robotics and Automation Letters. IEEE, 2021.

We used the following textbook as a reference to implement the Cross Entropy Method and the Nelder-Mead Simplex Method:

  • M. J. Kochenderfer and T. A. Wheeler, Algorithms for optimization. The MIT Press, 2019.

PETS

PETS is originally presented in the first paper. Note that we provide the MPC implementation but not the model-learning part. We referred to the second paper as well during the development of this software.

  • K. Chua, R. Calandra, R. McAllister, and S. Levine, "Deep reinforcement learning in a handful of trials using probabilistic dynamics models," in Advances in Neural Information Processing Systems, 2018, pp. 4754–4765.

  • A. Nagabandi, K. Konoglie, S. Levine, and V. Kumar, "Deep dynamics models for learning dexterous manipulation," in Conference on Robot Learning. PMLR, 2020, pp. 1101-1112.