research-article

Planning Reactive Manipulation in Dynamic Environments

Authors:

Philipp S. Schmitt,

Florian Wirnshofer,

Georg v. Wichert,

Wolfram BurgardAuthors Info & Claims

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Pages 136 - 143

https://doi.org/10.1109/IROS40897.2019.8968452

Published: 01 November 2019 Publication History

Abstract

When robots perform manipulation tasks, they need to determine their own movement, as well as how to make and break contact with objects in their environment. Reasoning about the motions of robots and objects simultaneously leads to a constrained planning problem in a high-dimensional state-space. Additionally, when environments change dynamically motions must be computed in real-time.To this end, we propose a feedback planner for manipulation. We model manipulation as constrained motion and use this model to automatically derive a set of constraint-based controllers. These controllers are used in a switching-control scheme, where the active controller is chosen by a reinforcement learning agent. Our approach is capable of addressing tasks with second-order dynamics, closed kinematic chains, and time-variant environments. We validated our approach in simulation and on a real, dual-arm robot. Extensive simulation of three distinct robots and tasks show a significant increase in robustness compared to a previous approach.

References

[1]

J. De Schutter, T. De Laet, J. Rutgeerts, W. Decré, R. Smits, E. Aertbeliën, K. Claes, and H. Bruyninckx, “Constraint-based task specification and estimation for sensor-based robot systems in the presence of geometric uncertainty,” The Int. Journal of Robotics Research, vol. 26, no. 5, pp. 433–455, 2007.

Digital Library

[2]

R. Alami, T. Simeon, and J.-P. Laumond, “A geometrical approach to planning manipulation tasks. the case of discrete placements and grasps,” in The fifth Int. Symposium on Robotics Research. MIT Press, 1990, pp. 453–463.

[3]

P. S. Schmitt, F. Wirnshofer, K. M. Wurm, G. v. Wichert, and W. Burgard, “Modeling and planning manipulation in dynamic environments,” in Int. Conf. on Robotics and Automation (accepted), preprint available at: http://ais.informatik.unifreiburg.de/publications/papers/schmitt19icra.pdf. IEEE, 2019.

[4]

J. Mirabel and F. Lamiraux, “Manipulation planning: addressing the crossed foliation issue,” in Int. Conf. on Robotics and Automation. IEEE, 2017, pp. 4032–4037.

[5]

W. Decré, R. Smits, H. Bruyninckx, and J. De Schutter, “Extending iTaSC to support inequality constraints and non-instantaneous task specification,” in Int. Conf. on Robotics and Automation. IEEE, 2009, pp. 964–971.

[6]

W. Decré, H. Bruyninckx, and J. De Schutter, “Extending the itasc constraint-based robot task specification framework to time-independent trajectories and user-configurable task horizons,” in Int.Conf. on Robotics and Automation. IEEE, 2013, pp. 1941–1948.

[7]

E. Aertbeliën and J. De Schutter, “eTaSL/eTC: A constraint-based task specification language and robot controller using expression graphs,” in Int. Conf. on Intelligent Robots and Systems. IEEE, 2014, pp. 1540–1546.

[8]

T. Simeon, J.-P. Laumond, J. Cortés, and A. Sahbani, “Manipulation planning with probabilistic roadmaps,” The Int. Journal of Robotics Research, vol. 23, no. 7-8, pp. 729–746, 2004.

[9]

K. Hauser, “Task planning with continuous actions and nondeterministic motion planning queries,” in AAAI Workshop on Bridging the Gap between Task and Motion Planning, 2010.

[10]

K. Hauser and J.-C. Latombe, “Multi-modal motion planning in non-expansive spaces,” The Int. Journal of Robotics Research, 2009.

[11]

C. R. Garrett, T. Lozano-Peréz, and L. P. Kaelbling, “FFRob: An efficient heuristic for task and motion planning,” in Algorithmic Foundations of Robotics XI. Springer, 2015, pp. 179–195.

[12]

C. R. Garrett, T. Lozano-Peréz, and L. P. Kaelbling, “Backward-forward search for manipulation planning,” in Int. Conf. on Intelligent Robots and Systems.IEEE, 2015, pp. 6366–6373.

[13]

W. Vega-Brown and N. Roy, “Asymptotically optimal planning under piecewise-analytic constraints,” The 12th Int. Workshop on the Algorithmic Foundations of Robotics, 2016.

[14]

P. S. Schmitt, W. Neubauer, W. Feiten, K. M. Wurm, G. v. Wichert, and W. Burgard, “Optimal, sampling-based manipulation planning,” in Int. Conf. on Robotics and Automation. IEEE, 2017, pp. 3426–3432.

[15]

D. Berenson, S. S. Srinivasa, D. Ferguson, and J. J. Kuffner, “Manipulation planning on constraint manifolds,” in Int. Conf. on Robotics and Automation. IEEE, 2009, pp. 625–632.

[16]

Z. Kingston, M. Moll, and L. E. Kavraki, “Sampling-based methods for motion planning with constraints,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 1, pp. 159–185, 2018.

[17]

H. I. Bozma and D. E. Koditschek, “Assembly as a noncooperative game of its pieces: analysis of 1d sphere assemblies,” Robotica, vol. 19, no. 1, pp. 93–108, 2001.

[18]

C. Karagoz, H. I. Bozma, and D. E. Koditschek, “Feedback-based event-driven parts moving,” Transactions on Robotics, vol. 20, no. 6, pp. 1012–1018, 2004.

Digital Library

[19]

V. Vasilopoulos, T. T. Topping, W. Vega-Brown, N. Roy, and D. E. Koditschek, “Sensor-based reactive execution of symbolic rearrangement plans by a legged mobile manipulator,” in Int. Conf. on Intelligent Robots and Systems., 2018.

[20]

M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. P. Abbeel, and W. Zaremba, “Hindsight experience replay,” in Advances in Neural Information Processing Systems, 2017, pp. 5048–5058.

[21]

M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, et al., “Learning dexterous in-hand manipulation,” arXiv preprint arXiv:1808.00177, 2018.

[22]

T. De Laet and J. De Schutter, “Constraint-based control of sensorbased robot systems with uncertain geometry,” Department of Mechanical Engineering, KU Leuven, Tech. Rep., 2007.

[23]

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, p. 529, 2015.

[24]

L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Transactions on Robotics and Automation, vol. 12, no. 4, pp. 566–580, 1996.

[25]

R. R. Burridge, A. A. Rizzi, and D. E. Koditschek, “Sequential composition of dynamically dexterous robot behaviors,” The Int. Journal of Robotics Research, vol. 18, no. 6, pp. 534–555, 1999.

[26]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Int. Conf. on Computer Vision and Pattern Recognition, 2016, pp. 770–778.

[27]

D.-A. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and accurate deep network learning by exponential linear units (elus),” in Int. Conf. on Learning Representations, 2016.

[28]

Z. Wang, T. Schaul, M. Hessel, H. Hasselt, M. Lanctot, and N. Freitas, “Dueling network architectures for deep reinforcement learning,” in Int. Conf. on Machine Learning, 2016, pp. 1995–2003.

[29]

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Int. Conf. on Learning Representations, 2015.

Cited By

Elguea-Aguinaco ÍInziarte-Hidalgo IBøgh SArana-Arexolaleiba N(2024)A Review on Reinforcement Learning for Motion Planning of Robotic ManipulatorsInternational Journal of Intelligent Systems10.1155/int/16364972024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/int/1636497
Dastider ARaza SLin MHong JBures MPark JCerny T(2022)Learning adaptive control in dynamic environments using reproducing kernel priors with bayesian policy gradientsProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507091(748-757)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3477314.3507091

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

6597 pages

Copyright © 2019.

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IEEE Press

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Published: 01 November 2019

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Cited By

Elguea-Aguinaco ÍInziarte-Hidalgo IBøgh SArana-Arexolaleiba N(2024)A Review on Reinforcement Learning for Motion Planning of Robotic ManipulatorsInternational Journal of Intelligent Systems10.1155/int/16364972024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/int/1636497
Dastider ARaza SLin MHong JBures MPark JCerny T(2022)Learning adaptive control in dynamic environments using reproducing kernel priors with bayesian policy gradientsProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507091(748-757)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3477314.3507091

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