Abstract

We address the problem of applying Task and Motion Planning (TAMP) in real world environments. TAMP combines symbolic and geometric reasoning to produce sequential manipulation plans, typically specified as joint-space trajectories, which are valid only as long as the environment is static and perception and control are highly accurate. In case of any changes in the environment, slow re-planning is required. We propose a TAMP algorithm that optimizes over Cartesian frames defined relative to target objects. The resulting plan then remains valid even if the objects are moving and can be executed by reactive controllers that adapt to these changes in real time. We apply our TAMP framework to a torque-controlled robot in a pick and place setting and demonstrate its ability to adapt to changing environments, inaccurate perception, and imprecise control, both in simulation and the real world.

Links

2020 RA-L: http://arxiv.org/abs/1911.04679

Code: http://github.com/tmigimatsu/logic-opt.git

Contact: takatoki {at} cs {dot} stanford {dot} edu

Bibtex

@article{migimatsu2019objectcentric,

    title={Object-Centric Task and Motion Planning in Dynamic Environments},

    author={Toki Migimatsu and Jeannette Bohg},

    journal={IEEE Robotics and Automation Letters},

    year={2020},

    volume={5},

    number={2},

    pages={844-851},

    doi={10.1109/LRA.2020.2965875},

}