Anytime Integrated Task and Motion Policies for Stochastic Environments

@article{Shah2020AnytimeIT,
  title={Anytime Integrated Task and Motion Policies for Stochastic Environments},
  author={N. Shah and Siddharth Srivastava},
  journal={2020 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2020},
  pages={9285-9291}
}
  • N. Shah, Siddharth Srivastava
  • Published 2020
  • Computer Science
  • 2020 IEEE International Conference on Robotics and Automation (ICRA)
  • In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstract models are typically lossy and plans or policies computed using them can be unexecutable. These problems are exacerbated in stochastic situations where the robot needs to reason about, and plan for multiple contingencies. We present a new approach for integrated task and motion planning in stochastic settings. In… CONTINUE READING
    1 Citations

    Figures and Topics from this paper.

    Multi-Robot Task and Motion Planning With Subtask Dependencies

    References

    SHOWING 1-10 OF 51 REFERENCES
    An Anytime Algorithm for Task and Motion MDPs
    • 4
    • PDF
    Guided search for task and motion plans using learned heuristics
    • 25
    • PDF
    Combined task and motion planning through an extensible planner-independent interface layer
    • 282
    • PDF
    Modular task and motion planning in belief space
    • 25
    • PDF
    Incremental Task and Motion Planning: A Constraint-Based Approach
    • 100
    • PDF
    Planning under Time Constraints in Stochastic Domains
    • 266
    • PDF
    Anytime Sensing Planning and Action: A Practical Model for Robot Control
    • 136
    • PDF
    Integrated task and motion planning in belief space
    • 250
    • PDF
    FFRob: Leveraging symbolic planning for efficient task and motion planning
    • 55
    • PDF
    An incremental constraint-based framework for task and motion planning
    • 36
    • PDF