Posted by Andy Zeng and Pete Florence, Research Scientists, Robotics at Google
Rearranging objects (such as organizing books on a bookshelf, moving utensils on a dinner table, or pushing piles of coffee beans) is a fundamental skill that can enable robots to physically interact with our diverse and unstructured world. While easy for people, accomplishing such tasks remains an open research challenge for embodied machine learning (ML) systems, as it requires both high-level and low-level perceptual reasoning. For example, when stacking a pile of books, one might consider where the books should be stacked, and in which order, while ensuring that the edges of the books align with each other to form a neat pile.
Across many application areas in ML, simple differences in model architecture can exhibit vastly different generalization properties. Therefore, one might ask whether there are certain deep network architectures that favor simple underlying elements of the rearrangement problem. Convolutional architectures, for example, are common in computer vision as they encode translational invariance, yielding the same response even if an image is shifted, while Transformer architectures are common in language processing because they exploit self-attention to capture long-range contextual dependencies. In robotics applications, one common architectural element is to use object-centric representations such as poses, keypoints, or object descriptors inside learned models, but these representations require additional training data (often manually annotated) and struggle to describe difficult scenarios such as deformables (e.g., playdough), fluids (honey), or piles of stuff (chopped onions).
Today, we present the Transporter Network, a simple model architecture for learning vision-based rearrangement tasks, which appeared as a publication and plenary talk during CoRL 2020. Transporter Nets use a novel approach to 3D spatial understanding that avoids reliance on object-centric representations, making them general for vision-based manipulation but far more sample efficient than benchmarked end-to-end alternatives. As a consequence, they are fast and practical to train on real robots. We are also releasing an accompanying open-source implementation of Transporter Nets together with Ravens, our new simulated benchmark suite of ten vision-based manipulation tasks.
Transporter Networks: Rearranging the Visual World for Robotic Manipulation
The key idea behind the Transporter Network architecture is that one can formulate the rearrangement problem as learning how to move a chunk of 3D space. Rather than relying on an explicit definition of objects (which is bound to struggle at capturing all edge cases), 3D space is a much broader definition for what could serve as the atomic units being rearranged, and can broadly encompass an object, part of an object, or multiple objects, etc. Transporter Nets leverage this structure by capturing a deep representation of the 3D visual world, then overlaying parts of it on itself to imagine various possible rearrangements of 3D space. It then chooses the rearrangements that best match those it has seen during training (e.g., from expert demonstrations), and uses them to parameterize robot actions. This formulation allows Transporter Nets to generalize to unseen objects and enables them to better exploit geometric symmetries in the data, so that they can extrapolate to new scene configurations. Transporter Nets are applicable to a wide variety of rearrangement tasks for robotic manipulation, expanding beyond our earlier models, such as affordance-based manipulation and TossingBot, that focus only on grasping and tossing.
| Transporter Nets capture a deep representation of the visual world, then overlay parts of it on itself to imagine various possible rearrangements of 3D space to find the best one and inform robot actions. |
Ravens Benchmark
To evaluate the performance of Transporter Nets in a consistent environment for fair comparisons to baselines and ablations, we developed Ravens, a benchmark suite of ten simulated vision-based rearrangement tasks. Ravens features a Gym API with a built-in stochastic oracle to evaluate the sample efficiency of imitation learning methods. Ravens avoids assumptions that cannot transfer to a real setup: observation data contains only RGB-D images and camera parameters; actions are end effector poses (transposed into joint positions with inverse kinematics).
Experiments on these ten tasks show that Transporter Nets are orders of magnitude more sample efficient than other end-to-end methods, and are capable of achieving over 90% success on many tasks with just 100 demonstrations, while the baselines struggle to generalize with the same amount of data. In practice, this makes collecting enough demonstrations a more viable option for training these models on real robots (which we show examples of below).
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| Our new Ravens benchmark includes ten simulated vision-based manipulation tasks, including pushing and pick-and-place, for which experiments show that Transporter Nets are orders of magnitude more sample efficient than other end-to-end methods. Ravens features a Gym API with a built-in stochastic oracle to evaluate the sample efficiency of imitation learning methods. |
Our new Ravens benchmark includes ten simulated vision-based manipulation tasks, including pushing and pick-and-place, for which experiments show that Transporter Nets are orders of magnitude more sample efficient than other end-to-end methods. Ravens features a Gym API with a built-in stochastic oracle to evaluate the sample efficiency of imitation learning methods.
Highlights
Given 10 example demonstrations, Transporter Nets can learn pick and place tasks such as stacking plates (surprisingly easy to misplace!), multimodal tasks like aligning any corner of a box to a marker on the tabletop, or building a pyramid of blocks.
By leveraging closed-loop visual feedback, Transporter Nets have the capacity to learn various multi-step sequential tasks with a modest number of demonstrations: such as moving disks for Tower of Hanoi, palletizing boxes, or assembling kits of new objects not seen during training. These tasks have considerably “long horizons”, meaning that to solve the task the model must correctly sequence many individual choices. Policies also tend to learn emergent recovery behaviors.
One surprising thing about these results was that beyond just perception, the models were starting to learn behaviors that resemble high-level planning. For example, to solve Towers of Hanoi, the models have to pick which disk to move next, which requires recognizing the state of the board based on the current visible disks and their positions. With a box-palletizing task, the models must locate the empty spaces of the pallet, and identify how new boxes can fit into those voids. Such behaviors are exciting because they suggest that with all the baked-in invariances, the model can focus its capacity on learning the more high-level patterns in manipulation.
Transporter Nets can also learn tasks that use any motion primitive defined by two end effector poses, such as pushing piles of small objects into a target set, or reconfiguring a deformable rope to connect the two end-points of a 3-sided square. This suggests that rigid spatial displacements can serve as useful priors for nonrigid ones.
Conclusion
Transporter Nets bring a promising approach to learning vision-based manipulation, but are not without limitations. For example, they can be susceptible to noisy 3D data, we have only demonstrated them for sparse waypoint-based control with motion primitives, and it remains unclear how to extend them beyond spatial action spaces to force or torque-based actions. But overall, we are excited about this direction of work, and we hope that it provides inspiration for extensions beyond the applications we’ve discussed. For more details, please check out our paper.
Acknowledgements
This research was done by Andy Zeng, Pete Florence, Jonathan Tompson, Stefan Welker, Jonathan Chien, Maria Attarian, Travis Armstrong, Ivan Krasin, Dan Duong, Vikas Sindhwani, and Johnny Lee, with special thanks to Ken Goldberg, Razvan Surdulescu, Daniel Seita, Ayzaan Wahid, Vincent Vanhoucke, Anelia Angelova, Kendra Byrne, for helpful feedback on writing; Sean Snyder, Jonathan Vela, Larry Bisares, Michael Villanueva, Brandon Hurd for operations and hardware support; Robert Baruch for software infrastructure, Jared Braun for UI contributions; Erwin Coumans for PyBullet advice; Laura Graesser for video narration.
