70 citations · 431 across the 35 of their papers we have counts for
7 papers · 1 filter
RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks
Christopher Xie, Arsalan Mousavian, Yu Xiang +1
Segmenting unseen object instances in cluttered environments is an important capability that robots need when functioning in unstructured environments. While previous methods have…
NeRP: Neural Rearrangement Planning for Unknown Objects
Ahmed H. Qureshi, Arsalan Mousavian, Chris Paxton +2
Robots will be expected to manipulate a wide variety of objects in complex and arbitrary ways as they become more widely used in human environments. As such, the rearrangement of o…
STORM: An Integrated Framework for Fast Joint-Space Model-Predictive Control for Reactive Manipulation
Mohak Bhardwaj, Balakumar Sundaralingam, Arsalan Mousavian +4
Sampling-based model-predictive control (MPC) is a promising tool for feedback control of robots with complex, non-smooth dynamics, and cost functions. However, the computationally…
RGB-D Local Implicit Function for Depth Completion of Transparent Objects
Luyang Zhu, Arsalan Mousavian, Yu Xiang +4
Majority of the perception methods in robotics require depth information provided by RGB-D cameras. However, standard 3D sensors fail to capture depth of transparent objects due to…
Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections
Yashraj Narang, Balakumar Sundaralingam, Miles Macklin +2
Tactile sensing is critical for robotic grasping and manipulation of objects under visual occlusion. However, in contrast to simulations of robot arms and cameras, current simulati…
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes
Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel +1
Grasping unseen objects in unconstrained, cluttered environments is an essential skill for autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning, ex…