Ensuring safety of policies learned by reinforcement: Reaching objects in the presence of obstacles with the iCub

@article{Pathak2013EnsuringSO,
  title={Ensuring safety of policies learned by reinforcement: Reaching objects in the presence of obstacles with the iCub},
  author={Shashank Pathak and Luca Pulina and Giorgio Metta and Armando Tacchella},
  journal={2013 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year={2013},
  pages={170-175}
}
Given a stochastic policy learned by reinforcement, we wish to ensure that it can be deployed on a robot with demonstrably low probability of unsafe behavior. Our case study is about learning to reach target objects positioned close to obstacles, and ensuring a reasonably low collision probability. Learning is carried out in a simulator to avoid physical damage in the trial-and-error phase. Once a policy is learned, we analyze it with probabilistic model checking tools to identify and correct… CONTINUE READING

Figures, Tables, and Topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 13 CITATIONS

Probabilistic Model Checking of Robots Deployed in Extreme Environments

VIEW 4 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Safety-critical advanced robots: A survey

  • Robotics and Autonomous Systems
  • 2017
VIEW 1 EXCERPT
CITES BACKGROUND

Periodic state-machine aware real-time analysis

  • 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA)
  • 2015
VIEW 1 EXCERPT
CITES BACKGROUND

References

Publications referenced by this paper.
SHOWING 1-10 OF 18 REFERENCES

DTMC Model Checking by SCC Reduction

  • 2010 Seventh International Conference on the Quantitative Evaluation of Systems
  • 2010
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL