Abstract
We propose a method for planning motion for robots with actuation uncertainty that incorporates contact with the environment and the compliance of the robot to reliably perform manipulation tasks. Our approach consists of two stages: (1) Generating partial policies using a sampling-based motion planner that uses particle-based models of uncertainty and simulation of contact and compliance; and (2) Resilient execution that updates the planned policies to account for unexpected behavior in execution which may arise from model or environment inaccuracy. We have tested our planner and policy execution in simulated SE(2) and SE(3) environments and Baxter robot. We show that our methods efficiently generate policies to perform manipulation tasks involving significant contact and compare against several simpler methods. Additionally, we show that our policy adaptation is resilient to significant changes during execution; e.g. adding a new obstacle to the environment.
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References
Lozano-Prez, T., Mason, M.T., Taylor, R.H.: Automatic synthesis of fine-motion strategies for robots. IJRR 3(1) (1984) 3–24
Goldman, R.P., Boddy, M.S.: Expressive planning and explicit knowledge. In: Artificial Intelligence Planning Systems. (May 1996)
Melchior, N.A., Simmons, R.: Particle rrt for path planning with uncertainty. In: ICRA. (April 2007)
Canny, J.: On computability of fine motion plans. In: ICRA. (May 1989)
Erdmann, M.: Using backprojections for fine motion planning with uncertainty. The International Journal of Robotics Research 5(1) (1986) 19–45
Kurniawati, H., Hsu, D., Lee, W.S.: Sarsop: Efficient point-based pomdp planning by approximating optimally reachable belief spaces. In: RSS. (2008)
Bai, H., Hsu, D., Kochenderfer, M., Lee, W.S.: Unmanned aircraft collision avoidance using continuous-state pomdps. In: RSS. (June 2011)
Koval, M., Pollard, N., Srinivasa, S.: Pre- and post-contact policy decomposition for planar contact manipulation under uncertainty. In: RSS. (July 2014)
Levine, S., Wagener, N., Abbeel, P.: Learning contact-rich manipulation skills with guided policy search. In: ICRA. (May 2015)
Roy, N., Prentice, S.: The belief roadmap: Efficient planning in belief space by factoring the covariance. IJRR 28(11-12) (2009) 1448–1465
Bry, A., Roy, N.: Rapidly-exploring random belief trees for motion planning under uncertainty. In: ICRA. (May 2011)
Agha-mohammadi, A.a., Chakravorty, S., Amato, N.M.: Firm: Sampling-based feedback motion planning under motion uncertainty and imperfect measurements. The International Journal of Robotics Research (2013)
Alterovitz, R., Simon, T., Goldberg, K.: The stochastic motion roadmap: A sampling framework for planning with markov motion uncertainty. In: RSS. (June 2007)
Littlefield, Z., Klimenko, D., Kurniawati, H., Bekris, K.E.: The importance of a suitable distance function in belief-space planning. In: ISRR. (September 2015)
Berg, J.V.D., Abbeel, P., Goldberg, K.: Lqg-mp: Optimized path planning for robots with motion uncertainty and imperfect state information. In: RSS. (June 2010)
Huynh, V.A., Karaman, S., Frazzoli, E.: An incremental sampling-based algorithm for stochastic optimal contro. In: ICRA. (May 2012)
Davis, B., Karamouzas, I., Guy, S.J.: C-opt: Coverage-aware trajectory optimization under uncertainty. IEEE Robotics and Automation Letters 1(2) (July 2016) 1020–1027
Lee, A., Duan, Y., Patil, S., Schulman, J., McCarthy, Z., van den Berg, J., Goldberg, K., Abbeel, P.: Sigma hulls for gaussian belief space planning for imprecise articulated robots amid obstacles. In: IROS. (Nov 2013)
Nieuwenhuisen, D., van der Stappen, A.F., Overmars, M.H.: Pushing using compliance. In: ICRA. (May 2006)
LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. IJRR 20(5) (2001) 378–400
Phillips-Grafflin, C., Berenson, D.: Planning and Resilient Execution of Policies For Manipulation in Contact with Actuation Uncertainty. arXiv preprint arXiv:1703.10261 (2017)
Sneath, P.H.A., Sokal, R.R.: Numerical taxonomy: the principles and practice of numerical classification. Freeman (1973)
Asafi, S., Goren, A., Cohen-Or, D.: Weak convex decomposition by lines-of-sight. Computer Graphics Forum 32(5) (2013) 23–31
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Phillips-Grafflin, C., Berenson, D. (2020). Planning and Resilient Execution of Policies For Manipulation in Contact with Actuation Uncertainty. In: Goldberg, K., Abbeel, P., Bekris, K., Miller, L. (eds) Algorithmic Foundations of Robotics XII. Springer Proceedings in Advanced Robotics, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-43089-4_48
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DOI: https://doi.org/10.1007/978-3-030-43089-4_48
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