93 citations · 129 across the 10 of their papers we have counts for
7 papers · 1 filter
Robust 2D Assembly Sequencing via Geometric Planning with Learned Scores
Tzvika Geft, Aviv Tamar, Ken Goldberg +1
To compute robust 2D assembly plans, we present an approach that combines geometric planning with a deep neural network. We train the network using the Box2D physics simulator with…
Refined Analysis of Asymptotically-Optimal Kinodynamic Planning in the State-Cost Space
Michal Kleinbort, Edgar Granados, Kiril Solovey +3
We present a novel analysis of AO-RRT: a tree-based planner for motion planning with kinodynamic constraints, originally described by Hauser and Zhou (AO-X, 2016). AO-RRT explores…
dRRT*: Scalable and Informed Asymptotically-Optimal Multi-Robot Motion Planning
Rahul Shome, Kiril Solovey, Andrew Dobson +2
Many exciting robotic applications require multiple robots with many degrees of freedom, such as manipulators, to coordinate their motion in a shared workspace. Discovering high-qu…
Fast, High-Quality Dual-Arm Rearrangement in Synchronous, Monotone Tabletop Setups
Rahul Shome, Kiril Solovey, Jingjin Yu +2
Rearranging objects on a planar surface arises in a variety of robotic applications, such as product packaging. Using two arms can improve efficiency but introduces new computation…
Effective Metrics for Multi-Robot Motion-Planning
Aviel Atias, Kiril Solovey, Oren Salzman +1
We study the effectiveness of metrics for Multi-Robot Motion-Planning (MRMP) when using RRT-style sampling-based planners. These metrics play the crucial role of determining the ne…
New perspective on sampling-based motion planning via random geometric graphs
Kiril Solovey, Oren Salzman, Dan Halperin
Roadmaps constructed by many sampling-based motion planners coincide, in the absence of obstacles, with standard models of random geometric graphs (RGGs). Those models have been st…