@article{jia2024learning,
  title = {Learning a Meta-Controller for Dynamic Grasping},
  author = {Jia, Yinsen and Xu, Jingxi and Jayaraman, Dinesh and Song, Shuran},
  abstract = {Grasping moving objects is a challenging task that requires multiple submodules such as object pose predictor, arm motion planner, etc. Each submodule operates under its own set of meta-parameters. For example, how far the pose predictor should look into the future (i.e., \textit{look-ahead time}) and the maximum amount of time the motion planner can spend planning a motion (i.e., \textit{time budget}). Many previous works assign fixed values to these parameters; however, at different moments \textit{within} a single episode of dynamic grasping, the optimal values should vary depending on the current scene. In this work, we propose a dynamic grasping pipeline with a meta-controller that controls the look-ahead time and time budget dynamically. We learn the meta-controller through reinforcement learning with a sparse reward. Our experiments show the meta-controller improves the grasping success rate (up to 28\% in the most cluttered environment) and reduces grasping time, compared to the strongest baseline. Our meta-controller learns to reason about the reachable workspace and maintain the predicted pose within the reachable region. In addition, it assigns a small but sufficient time budget for the motion planner. Our method can handle different objects, trajectories, and obstacles. Despite being trained only with 3-6 random cuboidal obstacles, our meta-controller generalizes well to 7-9 obstacles and more realistic out-of-domain household setups with unseen obstacle shapes.},
  journal = {CASE},
  year = {2024},
  month = {Aug},
  pub_type = {conference},
  url_pdf = {/publication/jia-2024-learning/jia-2024-learning.pdf},
  url_project = {https://yjia.net/meta},
  url_video = {https://www.youtube.com/watch?v=LbJq8EpQROI},
  url = {https://arxiv.org/abs/2302.08463},
  url_arxiv = {https://arxiv.org/abs/2302.08463},
  url_supp = {https://yjia.net/Assets/docs/case2024_dynamic_Appendix.pdf},
}