Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep learning methods have allowed rapid progress in robotic object grasping. In this systematic review, we surveyed the publications over the last decade, with a particular interest in grasping an object using all six degrees of freedom of the end-effector pose.

Fugatto is a versatile audio synthesis and transformation model capable of following
free-form text instructions with optional audio inputs. While large language
models (LLMs) trained with text on a simple next-token prediction objective can
learn to infer instructions directly from the data, models trained solely on audio
data lack this capacity. This is because audio data does not inherently contain the
instructions that were used to generate it. To overcome this challenge, we introduce

We analyze the weights of a trained speech-to-text neural network and discover a surprising amount of structure in the temporal convolutions. Based on our observations we propose to completely remove learnable temporal convolutions, and replace them with fixed averaging and shift operations which have no learnable parameters and open the way for significantly faster implementations. In the state-of-the-art models Conformer, Squeezeformer and FastConformer, this improves WER by 0.12%, 0.62%, and 0.20% respectively, while reducing the computational cost.

Extrinsic manipulation, the use of environment contacts to achieve manipulation objectives, enables strategies that are otherwise impossible with a parallel jaw gripper. However, orchestrating a long-horizon sequence of contact interactions between the robot, object, and environment is notoriously challenging due to the scene diversity, large action space, and difficult contact dynamics. We observe that most extrinsic manipulation are combinations of short-horizon primitives, each of which depend strongly on initializing from a desirable contact configuration to succeed.

Diffusion models have opened the path to a wide range of text-based image editing frameworks. However, these typically build on the multi-step nature of the diffusion backwards process, and adapting them to distilled, fast-sampling methods has proven surprisingly challenging. Here, we focus on a popular line of text-based editing frameworks - the ``edit-friendly'' DDPM-noise inversion approach. We analyze its application to fast sampling methods and categorize its failures into two classes: the appearance of visual artifacts, and insufficient editing strength.

Recent advancements in diffusion models have introduced fast sampling methods that can effectively produce high-quality images in just one or a few denoising steps. Interestingly, when these are distilled from existing diffusion models, they often maintain alignment with the original model, retaining similar outputs for similar prompts and seeds. These properties present opportunities to leverage fast sampling methods as a shortcut-mechanism, using them to create a preview of denoised outputs through which we can backpropagate image-space losses.

Large-scale text-to-image models enable a wide range of image editing techniques, using text prompts or even spatial controls. However, applying these editing methods to multi-view images depicting a single scene leads to 3D-inconsistent results. In this work, we focus on spatial control-based geometric manipulations and introduce a method to consolidate the editing process across various views.