Robotics design and construction to perform daily tasks is an exciting and one of the most challenging fields of computer science engineering. A team of researchers from MIT, NVIDIA, and Improbable AI Lab successfully programmed a Frank Panda robotic arm with a Robotiq 2F140 parallel jaw gripper for rearranging objects in a scene to achieve a desired object scene placing relationship. The existence of many geometrically similar rearrangement solutions for a given scene in the real world is not uncommon, and researchers build a solution using an iterative pose de-noising training procedure. 

The challenges faced in the real-world scenes are solving the present combinatorial variation in geometrical appearances and layout, which offer many locations and geometric features for object-scene interactions like placing a book in a half-filled rack or hanging mug in the mug stand. There may be many scene locations to place an object and these multiple possibilities lead to difficulties in programming, learning, and deployment. The system needs to predict multi-modal outputs that span the whole basis of possible rearrangements. 

For a given final object scene point clouds, the initial object configurations can be considered as perturbations from which the rearrangement can be predicted by point cloud pose de-noising. A noised point cloud can be generated from the final object-scene point cloud and randomly transferred to the initial configuration by training the model using neural networks. Multi-modality is ineffective for a given large data as the model tries to learn an average solution that fits the data poorly. The research team implemented multi-step noising processes and diffusion models to overcome this difficulty. The model is trained as a diffusion model and performs iterative de-noising.

Generalization to novel scene layouts is required after iterative de-noising. The research team proposes to locally encode the scene point cloud by cropping a region near the object. This helps the model concentrate on the data set in the neighborhood by ignoring the non-local distant distractors. Inference procedure from random guess may lead to a solution farther from a good solution. Researchers solve this by considering a larger crop size initially and reducing it upon multiple iterations to obtain a more local scene context.

The research team implemented Relational Pose Diffusion (RPDiff) to perform 6-DoF relational rearrangement conditioned on an object and scene point cloud. This generalizes across the various shapes, poses, and scene layouts with multi-modality. The motive they followed is to iteratively de-noise the 6-DoF pose of the object until it satisfies the desired geometrical relationship with the scene point cloud.

The research team uses RPDiff to perform relational rearrangement through pick-and-place on real-world objects and scenes. The model is successful in tasks such as placing a book on a partially filled bookshelf, stacking a can on an open shelf, and hanging a mug on the rack with many hooks. Their model can produce multi-modal distributions by overcoming multi-modal dataset fitting but also has limitations while working on pre-trained representations of data as their data for the demonstration was obtained only from scripted policies in simulation. Their work is related to other teams’ work on object rearrangement from perception by implementing Neural Shape Mating (NSM). 

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