52 citations · 67 across the 3 of their papers we have counts for
5 papers
Interpreting and Predicting Tactile Signals via a Physics-Based and Data-Driven Framework
Yashraj S. Narang, Karl Van Wyk, Arsalan Mousavian +1
High-density afferents in the human hand have long been regarded as essential for human grasping and manipulation abilities. In contrast, robotic tactile sensors are typically used…
A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Set
Clemens Eppner, Arsalan Mousavian, Dieter Fox
Robot grasping is often formulated as a learning problem. With the increasing speed and quality of physics simulations, generating large-scale grasping data sets that feed learning…
6-DOF Grasping for Target-driven Object Manipulation in Clutter
Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner +2
Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires both reasoning about unseen object parts and potential collisions with the manipulato…
LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation
Keunhong Park, Arsalan Mousavian, Yu Xiang +1
Current 6D object pose estimation methods usually require a 3D model for each object. These methods also require additional training in order to incorporate new objects. As a resul…
PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking
Xinke Deng, Arsalan Mousavian, Yu Xiang +3
Tracking 6D poses of objects from videos provides rich information to a robot in performing different tasks such as manipulation and navigation. In this work, we formulate the 6D o…