Chanyoung Ahn

Sparse matrices find numerous scientific and engineering applications, including simulations, optimizations, and large-scale data analysis. Characterized by a significant number of zero elements, sparse matrix solvers often encounter challenges in computational efficiency due to memory access patterns, becoming a computational bottleneck in many applications. We provide a comparative performance benchmark of two parallel solvers, SuperLU_dist and PaScaL_TDMA, for solving large sparse matrices. While SuperLU_dist, a widely recognized parallel solver developed under the Exascale Computing Project in the U.S., offers versatility and robustness across various sparse matrix structures, PaScaL_TDMA exhibits superior performance for tridiagonal matrices due to its tailored algorithmic design. These findings emphasize the importance of selecting optimized solvers based on the specific characteristics of the matrix structure in scientific and engineering computations. By leveraging the strengths of specialized solvers like PaScaL_TDMA, significant computational efficiencies can be achieved over general solvers, thereby accelerating the solution of large-scale problems in practical applications.

We aim to create a simulated testbed for training and assessing deformable object manipulation skills. This testbed requires tactile sensing to distinguish heterogeneous elasticities of a deformable object to acquire skills. In this work, we introduce a visuotactile testbed, DetactGym, for deformable object manipulation, integrating a novel architecture of tactile sensors leveraging collision cascades. These sensors integrate a diamond-shaped rigid element encased within an external rigid structure. This design overcomes a fundamental limitation of PhysX engine-based simulators: their inability to directly measure contact force on deformable objects. The diamond-shaped element efficiently transmits the contact force to the outer structure during interactions with these objects, ensuring force measurements. Our evaluation focuses on the testbed’s capability, equipped with tactile sensors, to facilitate the learning of lifting heterogeneous deformable objects with minimal deformation through reinforcement learning methods. Our findings highlight the effectiveness of tactile feedback over visual cues in manipulating deformable objects with diverse elasticities, reducing deformation.

Our aim is to create a human-expert manipulation dataset of deformable objects toward learning deformable manipulation. However, the data collection is challenging due to the limited sensing capability against to the high number of degree of freedom and complicate deformation/contact of soft bodies. Further, the self occlusion restricts the observation in grasping. In this work, we introduce a haptic-telemanipulation suite by adopting a haptic glove with a state-of-the-art physics simulator, IsaacGym from NVIDIA. The suite enables users to obtain realistic visual-and-tactile feedback as well as collect any part of object states while teleoperating a robotic gripper. We evaluate the suite by building one hundred demonstrations of dataset given five deformable objects.