MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by "seeing" through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items. Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches. The result is a new method that produces more accurate shape reconstructions, which could improve a robot's ability to reliably grasp and manipulate objects that are blocked from view. This new technique builds a partial reconstruction of a hidden object from reflected wireless signals and fills in the missing parts of its shape using a specially trained generative AI model.

This applies to common objects such as chairs, but also to novel objects that wehaveneverseenbefore.

Thus, they do not effectively capture implicit correspondences between articulated shapes or regularize jittery temporal deformations.

Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge.

We propose a new representation for encoding 3D shapes as neural fields. The representation isdesignedtobecompatible withthetransformer architecture and to benefit both shape reconstruction and shape generation. Existing works on neural fields aregrid-based representations withlatents defined onaregular grid.

We propose an end-to-end trainable, cross-category method for reconstructing multiple man-made articulated objects from a single RGBD image, focusing on part-level shape reconstruction and pose and kinematics estimation.

Our method utilizes animage-conditioned 3Dscene diffusion model to simultaneously denoise the 3D poses and geometries of all objects within the scene.

Our framework enables the development of the first fully data-driven solutions to active touch on top of learned models for object understanding.