1 citations · 1 across the 11 of their papers we have counts for
13 papers
Embodying Multi-Hand Manipulation Policies by Searching the Assignment and Null Spaces
Yorai Shaoul, Jiaoyang Li, Maxim Likhachev
Learned manipulation policies increasingly predict motions for abstract "hands" and are attractive in practice because they rely on easily collected demonstrations and transfer acr…
Model-Based Diffusion Optimal Control for Multi-Robot Motion Planning
Zhilin He, Yorai Shaoul, Jiaoyang Li
Multi-Robot Motion Planning in continuous environments, where robots must generate dynamically feasible, collision-free trajectories, is challenging due to the combinatorial growth…
Diffusion for Long-Horizon Multi-Robot Path Planning in Human-Shared Environments
Vaibhav Sanjay, Yorai Shaoul, Jiaoyang Li
Multi-robot path planning in human-shared environments requires a delicate balance between robust inter-robot coordination and socially aware behavior. While diffusion models excel…
LAMP: Long-Horizon Adaptive Manipulation Planning for Multi-Robot Collaboration in Cluttered Space
Shuai Zhou, Yorai Shaoul, Jiaoyang Li
Multi-robot manipulation requires jointly reasoning about contact formations, robot motions under coupled dynamics, and collision avoidance. Systematically searching over this larg…
Collaborative Multi-Robot Non-Prehensile Manipulation via Flow-Matching Co-Generation
Yorai Shaoul, Zhe Chen, Mohamed Naveed Gul Mohamed +3
Coordinating a team of robots to reposition multiple objects in cluttered environments requires reasoning jointly about where robots should establish contact, how to manipulate obj…
Conflict-Based Search as a Protocol: A Multi-Agent Motion Planning Protocol for Heterogeneous Agents, Solvers, and Independent Tasks
Rishi Veerapaneni, Alvin Tang, Haodong He +9
Imagine the future construction site, hospital, or office with dozens of robots bought from different manufacturers. How can we enable these different robots to effectively move in…
SRMP: Search-Based Robot Motion Planning Library
Itamar Mishani, Yorai Shaoul, Ramkumar Natarajan +2
Motion planning is a critical component in any robotic system. Over the years, powerful tools like the Open Motion Planning Library (OMPL) have been developed, offering numerous mo…
Benchmarking Shortcutting Techniques for Multi-Robot-Arm Motion Planning
Philip Huang, Yorai Shaoul, Jiaoyang Li
Generating high-quality motion plans for multiple robot arms is challenging due to the high dimensionality of the system and the potential for inter-arm collisions. Traditional mot…
MOSAIC: Skill-Centric Manipulation Planning with Physics Simulation
Itamar Mishani, Yorai Shaoul, Maxim Likhachev
Planning long-horizon manipulation motions using a set of predefined skills is a central challenge in robotics; solving it efficiently could enable general-purpose robots to tackle…
Multi-Robot Motion Planning with Diffusion Models
Yorai Shaoul, Itamar Mishani, Shivam Vats +2
Diffusion models have recently been successfully applied to a wide range of robotics applications for learning complex multi-modal behaviors from data. However, prior works have mo…
Unconstraining Multi-Robot Manipulation: Enabling Arbitrary Constraints in ECBS with Bounded Sub-Optimality
Yorai Shaoul, Rishi Veerapaneni, Maxim Likhachev +1
Multi-Robot-Arm Motion Planning (M-RAMP) is a challenging problem featuring complex single-agent planning and multi-agent coordination. Recent advancements in extending the popular…
Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences
Yorai Shaoul, Itamar Mishani, Maxim Likhachev +1
An exciting frontier in robotic manipulation is the use of multiple arms at once. However, planning concurrent motions is a challenging task using current methods. The high-dimensi…
Online Descriptor Enhancement via Self-Labelling Triplets for Visual Data Association
Yorai Shaoul, Katherine Liu, Kyel Ok +1
Object-level data association is central to robotic applications such as tracking-by-detection and object-level simultaneous localization and mapping. While current learned visual…