Imitation Learning (IL) is a promising paradigm for learning dynamic manipulation of deformable objects since it does not depend on difficult-to-create accurate simulations of such objects. However, the translation of motions demonstrated by a human to a robot is a challenge for IL, due to differences in the embodiments and the robot’s physical limits. These limits are especially relevant in dynamic manipulation where high velocities and accelerations are typical. 

To address this problem, we propose a framework that first maps a dynamic demonstration into a motion that respects the robot’s constraints using a constrained Dynamic Movement Primitive. Second, the resulting object state is further optimized by quasi-static refinement motions to optimize task performance metrics. This allows both efficiently altering the object state by dynamic motions and stable small-scale refinements. We evaluate the framework in the challenging task of bag opening, designing the system BILBO: Bimanual dynamic manipulation using Imitation Learning for Bag Opening. Our results show that BILBO can successfully open a wide range of crumpled bags, using a demonstration with a single bag. 

Short descriptive video

We evaluate BILBO on five different bags with the following properties:

• Bag A: biodegradable plastic, 38 cm x 40 cm

• Bag B: polyethylene, 41 cm x 34 cm

• Bag C: polyethylene, the rim contains a drawstring, 42 cm x 49 cm

• Bag D: polyethylene, 49 cm x 41 cm

• Bag E: polyethylene, the rim contains a drawstring, 53 cm x 59 cm

In the video BILBO is compared against a constrained DMP baseline, TC-DMP:

• BILBO: Encodes the demonstrated dynamic fling using the constrained DMP from (https://doi.org/10.1007/s10514-022-10067-4) and uses a linear motion for refining the rim roundness.

• TC-DMP: Replaces the constrained DMP in BILBO with the method from (https://doi.org/10.1109/LRA.2021.3058874).

Thesis

The paper is based on the research done in Eric Hannus' Master's thesis (available at https://aaltodoc.aalto.fi/items/05333c14-4fa7-46f7-bcbe-7954b0754aa6). See this document for more details about, for example, processing of the bag state and experiment practicalities.

Additional examples

Below are some example videos at original speed of ideal runs which open the bag with a single or a only a few dynamic flings.

Citation

To cite this work, please use the following BibTex entry:

@inproceedings{hannus2024dynamic,

      title={Dynamic Manipulation of Deformable Objects using Imitation Learning with Adaptation to Hardware Constraints}, 

      author={Eric Hannus and Tran Nguyen Le and David Blanco-Mulero and Ville Kyrki},

      year={2024},

      journal = {arXiv preprint arXiv:2403.12685},

      eprint={2403.12685},

      archivePrefix={arXiv},

      primaryClass={cs.RO}

}