Skip to main content
    • Aa
    • Aa

Selection of trajectory parameters for dynamic pouring tasks based on exploitation-driven updates of local metamodels

  • Joshua D. Langsfeld (a1), Krishnanand N. Kaipa (a2) and Satyandra K. Gupta (a3)

We present an approach that allows a robot to generate trajectories to perform a set of instances of a task using few physical trials. Specifically, we address manipulation tasks which are highly challenging to simulate due to complex dynamics. Our approach allows a robot to create a model from initial exploratory experiments and subsequently improve it to find trajectory parameters to successfully perform a given task instance. First, in a model generation phase, local models are constructed in the vicinity of previously conducted experiments that explain both task function behavior and estimated divergence of the generated model from the true model when moving within the neighborhood of each experiment. Second, in an exploitation-driven updating phase, these generated models are used to guide parameter selection given a desired task outcome and the models are updated based on the actual outcome of the task execution. The local models are built within adaptively chosen neighborhoods, thereby allowing the algorithm to capture arbitrarily complex function landscapes. We first validate our approach by testing it on a synthetic non-linear function approximation problem, where we also analyze the benefit of the core approach features. We then show results with a physical robot performing a dynamic fluid pouring task. Real robot results reveal that the correct pouring parameters for a new pour volume can be learned quite rapidly, with a limited number of exploratory experiments.

Corresponding author
*Corresponding author. E-mail:
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

2. F. J. Abu-Dakka , F. J. Valero , J. Luis Suner and V. A , “Mata direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments,” Robotica 33 (3), 669683 (2015).

3. B. Akgun , M. Cakmak , K. Jiang and A. L. Thomaz , “Keyframe-based learning from demonstration,” Int. J. Soc. Robot. 4 (4), 343355 (2012).

4. H. F. N. Al-Shuka , B. Corves and W.-H. Zhu , “Function approximation technique-based adaptive virtual decomposition control for a serial-chain manipulator,” Robotica 32 (3), 375399 (2014).

5. M. Arif , T. Ishihara and H. Inooka , “Incorporation of experience in iterative learning controllers using locally weighted learning,” Automatica 37 (6), 881888 (2001).

6. C. G. Atkeson , A. W. Moore and S. Schaal , “Locally weighted learning,” Artif. Intell. 11, 1173 (1997).

9. C. Bowen , G. Ye and R. Alterovitz , “Asymptotically optimal motion planning for learned tasks using time-dependent cost maps,” IEEE Trans. Autom. Sci. Eng. 12 (1), 171182 (2015).

12. A. Broun , C. Beck , T. Pipe , M. Mirmehdi and C. Melhuish , “Bootstrapping a robot's kinematic model,” Robot. Auton. Syst. 62 (3), 330339 (2014).

15. A. El-Fakdi and M. Carreras , “Two-step gradient-based reinforcement learning for underwater robotics behavior learning,” Robotics and Autonomous Systems 61 (3), 271282 (2013).

16. H. Esfandiar , S. Daneshmand and R. D. Kermani , “On the control of a single flexible arm robot via Youla-Kucera parameterization,” Robotica 34 (01), 150172 (2016).

30. G. Pajak and I. Pajak , “Sub-optimal trajectory planning for mobile manipulators,” Robotica 33 (06), 11811200 (2015).

31. C. Park , J. Pan and D. Manocha , “High-DOF robots in dynamic environments using incremental trajectory optimization,” Int. J. Humanoid Robot. 11 (02) (2014).

33. J. Peters and S. Schaal , “Reinforcement learning of motor skills with policy gradients,” Neural Netw. 21, 682697 (2008).

35. M. Posa and R. Tedrake , “Direct Trajectory Optimization of Rigid Body Dynamical Systems Through Contact,” In: Algorithmic Foundations of Robotics X ( E. Frazzoli , T. Lozano-Perez , N. Roy , D. Rus , eds.), volume 86 (Springer Berlin Heidelberg, 2013) pp. 527542.

38. L. Rozo , P. Jimenez and C. Torras , “Force-Based Robot Learning of Pouring Skills using Parametric Hidden Markov Models,” International Workshop on Robot Motion and Control, RoMoCo, Wasowo, Poland (Jul. 2013) pp. 227232.

41. M. Tamosiunaite , B. Nemec , A. Ude and F. Wörgötter , “Learning to pour with a robot arm combining goal and shape learning for dynamic movement primitives,” Robot. Auton. Syst. 59 (11), 910922 (2011).

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

  • ISSN: 0263-5747
  • EISSN: 1469-8668
  • URL: /core/journals/robotica
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Full text views

Total number of HTML views: 0
Total number of PDF views: 33 *
Loading metrics...

Abstract views

Total abstract views: 170 *
Loading metrics...

* Views captured on Cambridge Core between 8th May 2017 - 22nd September 2017. This data will be updated every 24 hours.