87 citations · 279 across the 16 of their papers we have counts for
16 papers
Fast-Grasp'D: Dexterous Multi-finger Grasp Generation Through Differentiable Simulation
Dylan Turpin, Tao Zhong, Shutong Zhang +8
Multi-finger grasping relies on high quality training data, which is hard to obtain: human data is hard to transfer and synthetic data relies on simplifying assumptions that reduce…
Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands
Dylan Turpin, Liquan Wang, Eric Heiden +5
The study of hand-object interaction requires generating viable grasp poses for high-dimensional multi-finger models, often relying on analytic grasp synthesis which tends to produ…
Factory: Fast Contact for Robotic Assembly
Yashraj Narang, Kier Storey, Iretiayo Akinola +9
Robotic assembly is one of the oldest and most challenging applications of robotics. In other areas of robotics, such as perception and grasping, simulation has rapidly accelerated…
Accelerated Policy Learning with Parallel Differentiable Simulation
Jie Xu, Viktor Makoviychuk, Yashraj Narang +4
Deep reinforcement learning can generate complex control policies, but requires large amounts of training data to work effectively. Recent work has attempted to address this issue…
DefGraspSim: Physics-based simulation of grasp outcomes for 3D deformable objects
Isabella Huang, Yashraj Narang, Clemens Eppner +5
Robotic grasping of 3D deformable objects (e.g., fruits/vegetables, internal organs, bottles/boxes) is critical for real-world applications such as food processing, robotic surgery…
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting
Eric Heiden, Miles Macklin, Yashraj Narang +3
Robotic cutting of soft materials is critical for applications such as food processing, household automation, and surgical manipulation. As in other areas of robotics, simulators c…