3 citations · 9 across the 27 of their papers we have counts for
29 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…
Front-to-Attractors: Modifying the Front-to-Front Heuristic in Bidirectional Search
Alvin Zou, Muhammad Suhail Saleem, Maxim Likhachev
Heuristics play a central role in the performance of bidirectional search algorithms, which commonly rely on two main classes. Front-to-end (F2E) heuristics estimate the distance f…
Multi Graph Search for High-Dimensional Robot Motion Planning
Itamar Mishani, Maxim Likhachev
Efficient motion planning for high-dimensional robotic systems, such as manipulators and mobile manipulators, is critical for real-time operation and reliable deployment. Although…
Constant-Time Planning for Chaining Collision-free Motion to Manipulation Behaviors
Nayesha Gandotra, Itamar Mishani, Maxim Likhachev +2
Recent progress in contact-rich robotic manipulation has been striking, yet most deployed systems remain confined to simple, scripted routines. One of the barriers is the lack of m…
Think Fast: Real-Time Kinodynamic Belief-Space Planning for Projectile Interception
Gabriel Olin, Lu Chen, Nayesha Gandotra +2
Intercepting fast moving objects, by its very nature, is challenging because of its tight time constraints. This problem becomes further complicated in the presence of sensor noise…
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…
A Contact-Driven Framework for Manipulating in the Blind
Muhammad Suhail Saleem, Lai Yuan, Maxim Likhachev
Robots often face manipulation tasks in environments where vision is inadequate due to clutter, occlusions, or poor lighting--for example, reaching a shutoff valve at the back of a…
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…
Parallel Heuristic Search as Inference for Actor-Critic Reinforcement Learning Models
Hanlan Yang, Itamar Mishani, Luca Pivetti +2
Actor-Critic models are a class of model-free deep reinforcement learning (RL) algorithms that have demonstrated effectiveness across various robot learning tasks. While considerab…
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…
Dynamic Agent Grouping ECBS: Scaling Windowed Multi-Agent Path Finding with Completeness Guarantees
Tiannan Zhang, Rishi Veerapaneni, Shao-Hung Chan +2
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for a team of agents. Although several MAPF methods which solve full-horizon MAPF have compl…
Planning from Point Clouds over Continuous Actions for Multi-object Rearrangement
Kallol Saha, Amber Li, Angela Rodriguez-Izquierdo +4
Long-horizon planning for robot manipulation is a challenging problem that requires reasoning about the effects of a sequence of actions on a physical 3D scene. While traditional t…
Lazy Heuristic Search for Solving POMDPs with Expensive-to-Compute Belief Transitions
Muhammad Suhail Saleem, Rishi Veerapaneni, Maxim Likhachev
Heuristic search solvers like RTDP-Bel and LAO* have proven effective for computing optimal and bounded sub-optimal solutions for Partially Observable Markov Decision Processes (PO…
Optimal Interactive Learning on the Job via Facility Location Planning
Shivam Vats, Michelle Zhao, Patrick Callaghan +4
Collaborative robots must continually adapt to novel tasks and user preferences without overburdening the user. While prior interactive robot learning methods aim to reduce human e…
Anytime Single-Step MAPF Planning with Anytime PIBT
Nayesha Gandotra, Rishi Veerapaneni, Muhammad Suhail Saleem +3
PIBT is a popular Multi-Agent Path Finding (MAPF) method at the core of many state-of-the-art MAPF methods including LaCAM, CS-PIBT, and WPPL. The main utility of PIBT is that it i…
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…
Real-Time LaCAM for Real-Time MAPF
Runzhe Liang, Rishi Veerapaneni, Daniel Harabor +2
The vast majority of Multi-Agent Path Finding (MAPF) methods with completeness guarantees require planning full-horizon paths. However, planning full-horizon paths can take too lon…
Implicit Graph Search for Planning on Graphs of Convex Sets
Ramkumar Natarajan, Chaoqi Liu, Howie Choset +1
Graphs of Convex Sets (GCS) is a recent method for synthesizing smooth trajectories by decomposing the planning space into convex sets, forming a graph to encode the adjacency rela…
RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation
Shivam Vats, Devesh K. Jha, Maxim Likhachev +2
Model-based planners and controllers are commonly used to solve complex manipulation problems as they can efficiently optimize diverse objectives and generalize to long horizon tas…
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…
Windowed MAPF with Completeness Guarantees
Rishi Veerapaneni, Muhammad Suhail Saleem, Jiaoyang Li +1
Traditional multi-agent path finding (MAPF) methods try to compute entire start-goal paths which are collision free. However, computing an entire path can take too long for MAPF sy…
A POMDP-based hierarchical planning framework for manipulation under pose uncertainty
Muhammad Suhail Saleem, Rishi Veerapaneni, Maxim Likhachev
Robots often face challenges in domestic environments where visual feedback is ineffective, such as retrieving objects obstructed by occlusions or finding a light switch in the dar…
Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large Scale Imitation Learning for MAPF
Rishi Veerapaneni, Arthur Jakobsson, Kevin Ren +3
Multi-Agent Path Finding (MAPF) is the problem of effectively finding efficient collision-free paths for a group of agents in a shared workspace. The MAPF community has largely foc…
A Data Efficient Framework for Learning Local Heuristics
Rishi Veerapaneni, Jonathan Park, Muhammad Suhail Saleem +1
With the advent of machine learning, there have been several recent attempts to learn effective and generalizable heuristics. Local Heuristic A* (LoHA*) is one recent method that i…