Posted by Daniel Seita, Research Intern and Andy Zeng, Research Scientist, Robotics at Google
While the robotics research community has driven recent advances that enable robots to grasp a wide range of rigid objects, less research has been devoted to developing algorithms that can handle deformable objects. One of the challenges in deformable object manipulation is that it is difficult to specify such an object’s configuration. For example, with a rigid cube, knowing the configuration of a fixed point relative to its center is sufficient to describe its arrangement in 3D space, but a single point on a piece of fabric can remain fixed while other parts shift. This makes it difficult for perception algorithms to describe the complete “state” of the fabric, especially under occlusions. In addition, even if one has a sufficiently descriptive state representation of a deformable object, its dynamics are complex. This makes it difficult to predict the future state of the deformable object after some action is applied to it, which is often needed for multi-step planning algorithms.
In “Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks,” to appear at ICRA 2021, we release an open-source simulated benchmark, called DeformableRavens, with the goal of accelerating research into deformable object manipulation. DeformableRavens features 12 tasks that involve manipulating cables, fabrics, and bags and includes a set of model architectures for manipulating deformable objects towards desired goal configurations, specified with images. These architectures enable a robot to rearrange cables to match a target shape, to smooth a fabric to a target zone, and to insert an item in a bag. To our knowledge, this is the first simulator that includes a task in which a robot must use a bag to contain other items, which presents key challenges in enabling a robot to learn more complex relative spatial relations.
The DeformableRavens Benchmark
DeformableRavens expands our prior
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