Learning local trajectories for high precision robotic tasks: Application to KUKA LBR iiwa Cartesian positioning

@article{Gurin2016LearningLT,
  title={Learning local trajectories for high precision robotic tasks: Application to KUKA LBR iiwa Cartesian positioning},
  author={Joris Gu{\'e}rin and Olivier Gibaru and Eric Nyiri and St{\'e}phane Thiery},
  journal={IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society},
  year={2016},
  pages={5316-5321}
}
To ease the development of robot learning in industry, two conditions need to be fulfilled. Manipulators must be able to learn high accuracy and precision tasks while being safe for workers in the factory. In this paper, we extend previously submitted work which consist in rapid learning of local high accuracy behaviors. By exploration and regression, linear and quadratic models are learnt for respectively the dynamics and cost function. Iterative Linear Quadratic Gaussian Regulator combined… 

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References

SHOWING 1-10 OF 18 REFERENCES

Locally optimal control under unknown dynamics with learnt cost function: application to industrial robot positioning

TLDR
This work proposes a method to learn the cost function directly from the data, in the same way as for the dynamics, which can be defined in terms of any measurable quantity and thus can be chosen more appropriately for the task to be carried out.

Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning

TLDR
It is demonstrated how a low-cost off-the-shelf robotic system can learn closed-loop policies for a stacking task in only a handful of trials-from scratch.

Adaptive Optimal Feedback Control with Learned Internal Dynamics Models

TLDR
This chapter combines the ILQG framework with learning the forward dynamics for simulated arms, which exhibit large redundancies, both, in kinematics and in the actuation to demonstrate how the approach can compensate for complex dynamic perturbations in an online fashion.

Learning contact-rich manipulation skills with guided policy search

TLDR
This paper extends a recently developed policy search method and uses it to learn a range of dynamic manipulation behaviors with highly general policy representations, without using known models or example demonstrations, and shows that this method can acquire fast, fluent behaviors after only minutes of interaction time.

Path Planning for a Robot Manipulator based on Probabilistic Roadmap and Reinforcement Learning

TLDR
This paper proposes efficient real-time path planning by combining PRM and reinforcement learning to deal with uncertain dynamic environments and similar environments and demonstrates that the proposed hybrid path planner can generate a collision-free path even for dynamic environments in which objects block the pre-planned global path.

Reinforcement learning in robotics: A survey

TLDR
This article attempts to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots by highlighting both key challenges in robot reinforcement learning as well as notable successes.

Hinfinity reinforcement learning control of robot manipulators using fuzzy wavelet networks

A Survey on Policy Search for Robotics

TLDR
This work classifies model-free methods based on their policy evaluation strategy, policy update strategy, and exploration strategy and presents a unified view on existing algorithms.

Synthesis and stabilization of complex behaviors through online trajectory optimization

We present an online trajectory optimization method and software platform applicable to complex humanoid robots performing challenging tasks such as getting up from an arbitrary pose on the ground

A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems

  • E. TodorovWeiwei Li
  • Mathematics
    Proceedings of the 2005, American Control Conference, 2005.
  • 2005
We present an iterative linear-quadratic-Gaussian method for locally-optimal feedback control of nonlinear stochastic systems subject to control constraints. Previously, similar methods have been