Collision-free motion generation in unknown environments is a core building block for robot manipulation. Generating such motions is challenging due to multiple objectives; not only should the solutions be optimal, the motion generator itself must be fast enough for real-time performance and reliable enough for practical deployment. A wide variety of methods have been proposed ranging from local controllers to global planners, often being combined to offset their shortcomings.

We address the important problem of generalizing robotic rearrangement to clutter without any explicit object models. We first generate over 650K cluttered scenes— orders of magnitude more than prior work—in diverse everyday environments, such as cabinets and shelves. We render synthetic partial point clouds from this data and use it to train our CabiNet model architecture. CabiNet is a collision model that accepts object and scene point clouds, captured from a single-view depth observation, and predicts collisions for SE(3) object poses in the scene.

Computational lithography is a critical research area for the continued scaling of semiconductor manufacturing process technology by enhancing silicon printability via numerical computing methods. Today's solutions for these problems are primarily CPU-based and require many thousands of CPUs running for days to tape out a modern chip. We seek AI/GPU-assisted solutions for the two problems, aiming at improving both runtime and quality.

Arithmetic block identification in gate-level netlists plays an essential role for various purposes, including malicious logic detection, functional verification, or macro-block optimization. However, current methods usually suffer from either low performance or poor scalability. To address the issue, we come up with a novel framework based on graph learning and network flow analysis, that extracts desired logic components from a complete circuit netlist.

Lithography is fundamental to integrated circuit fabrication, necessitating large computation overhead. The advancement of machine learning (ML)-based lithography models alleviates the trade-offs between manufacturing process expense and capability. However, all previous methods regard the lithography system as an image-to-image black box mapping, utilizing network parameters to learn by rote mappings from massive mask-to-aerial or mask-to-resist image pairs, resulting in poor generalization capability.

Objects rarely sit in isolation in human environments. As such, we'd like our robots to reason about how multiple objects relate to one another and how those relations may change as the robot interacts with the world. To this end, we propose a novel graph neural network framework for multi-object manipulation to predict how inter-object relations change given robot actions. Our model operates on partial-view point clouds and can reason about multiple objects dynamically interacting during the manipulation.