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 work on rearranging objects and includes a suite of 12 simulated tasks involving 1D, 2D, and 3D deformable structures. Each task contains a simulated UR5 arm with a mock gripper for pinch grasping, and is bundled with scripted demonstrators to autonomously collect data for imitation learning. Tasks randomize the starting state of the items within a distribution to test generality to different object configurations.
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| Examples of scripted demonstrators for manipulation of 1D (cable), 2D (fabric), and 3D (bag) deformable structures in our simulator, using PyBullet. These show three of the 12 tasks in DeformableRavens. Left: the task is to move the cable so it matches the underlying green target zone. Middle: the task is to wrap the cube with the fabric. Right: the task is to insert the item in the bag, then to lift and move the bag to the square target zone. |
Specifying goal configurations for manipulation tasks can be particularly challenging with deformable objects. Given their complex dynamics and high-dimensional configuration spaces, goals cannot be as easily specified as a set of rigid object poses, and may involve complex relative spatial relations, such as “place the item inside the bag”. Hence, in addition to tasks defined by the distribution of scripted demonstrations, our benchmark also contains goal-conditioned tasks that are specified with goal images. For goal-conditioned tasks, a given starting configuration of objects must be paired with a separate image that shows the desired configuration of those same objects. A success for that particular case is then based on whether the robot is able to get the current configuration to be sufficiently close to the configuration conveyed in the goal image.
Goal-Conditioned Transporter Networks
To complement the goal-conditioned tasks in our simulated benchmark, we integrated goal-conditioning into our previously released Transporter Network architecture — an action-centric model architecture that works well on rigid object manipulation by rearranging deep features to infer spatial displacements from visual input. The architecture takes as input both an image of the current environment and a goal image with a desired final configuration of objects, computes deep visual features for both images, then combines the features using element-wise multiplication to condition pick and place correlations to manipulate both the rigid and deformable objects in the scene. A strength of the Transporter Network architecture is that it preserves the spatial structure of the visual images, which provides inductive biases that reformulate image-based goal conditioning into a simpler feature matching problem and improves the learning efficiency with convolutional networks.
An example task involving goal-conditioning is shown below. In order to place the green block into the yellow bag, the robot needs to learn spatial features that enable it to perform a multi-step sequence of actions to spread open the top opening of the yellow bag, before placing the block into it. After it places the block into the yellow bag, the demonstration ends in a success. If in the goal image the block were placed in the blue bag, then the demonstrator would need to put the block in the blue bag.
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| An example of a goal-conditioned task in DeformableRavens. Left: A frontal camera view of the UR5 robot and the bags, plus one item, in a desired goal configuration. Middle: The top-down orthographic image of this setup, which is size 160×320 and passed as the goal image to specify the task success criterion. Right: A video of the demonstration policy showing that the item goes into the yellow bag, instead of the blue one. |
Results
Our results suggest that goal-conditioned Transporter Networks enable agents to manipulate deformable structures into flexibly specified configurations without test-time visual anchors for target locations. We also significantly extend prior results using Transporter Networks for manipulating deformable objects by testing on tasks with 2D and 3D deformables. Results additionally suggest that the proposed approach is more sample-efficient than alternative approaches that rely on using ground-truth pose and vertex position instead of images as input.
For example, the learned policies can effectively simulate bagging tasks, and one can also provide a goal image so that the robot must infer into which bag the item should be placed.
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| An example of policies trained using Transporter Networks applied in action on bagging tasks, where the objective is to first open the bag, then to put one (left) or two (right) items in the bag, then to insert the bag into the target zone. The left animation is zoomed in for clarity. |
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| An example of the learned policy using Goal-Conditioned Transporter Networks. Left: The frontal camera view. Middle: The goal image that the Goal-Conditioned Transporter Network receives as input, which shows that the item should go in the red bag, instead of the blue distractor bag. Right: The learned policy putting the item in the red bag, instead of the distractor bag (colored yellow in this case). |
We encourage other researchers to check out our open-source code to try the simulated environments and to build upon this work. For more details, please check out our paper.
Future Work
This work exposes several directions for future development, including the mitigation of observed failure modes. As shown below, one failure is when the robot pulls the bag upwards and causes the item to fall out. Another is when the robot places the item on the irregular exterior surface of the bag, which causes the item to fall off. Future algorithmic improvements might allow actions that operate at a higher frequency rate, so that the robot can react in real time to counteract such failures.
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| Examples of failure cases from the learned Transporter-based policies on bag manipulation tasks. Left: the robot inserts the cube into the opening of the bag, but the bag pulling action fails to enclose the cube. Right: the robot fails to insert the cube into the opening, and is unable to perform recovery actions to insert the cube in a better location. |
Another area for advancement is to train Transporter Network-based models for deformable object manipulation using techniques that do not require expert demonstrations, such as example-based control or model-based reinforcement learning. Finally, the ongoing pandemic limited access to physical robots, so in future work we will explore the necessary ingredients to get a system working with physical bags, and to extend the system to work with different types of bags.
Acknowledgments
This research was conducted during Daniel Seita’s internship at Google’s NYC office in Summer 2020. We thank our collaborators Pete Florence, Jonathan Tompson, Erwin Coumans, Vikas Sindhwani, and Ken Goldberg.
