Maxim Likhachev
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View article: Conflict-Based Search as a Protocol: A Multi-Agent Motion Planning Protocol for Heterogeneous Agents, Solvers, and Independent Tasks
Conflict-Based Search as a Protocol: A Multi-Agent Motion Planning Protocol for Heterogeneous Agents, Solvers, and Independent Tasks Open
Imagine the future construction site, hospital, office, or even sophisticated household with dozens of robots bought from different manufacturers. How can we enable these different systems to effectively move in a shared environment, given…
View article: Dynamic Agent Grouping ECBS: Scaling Windowed Multi-Agent Path Finding with Completeness Guarantees
Dynamic Agent Grouping ECBS: Scaling Windowed Multi-Agent Path Finding with Completeness Guarantees Open
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 completeness guarantees, very few MAPF methods that plan partial…
View article: Planning from Point Clouds over Continuous Actions for Multi-object Rearrangement
Planning from Point Clouds over Continuous Actions for Multi-object Rearrangement Open
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 task planning methods are shown to be effective for long-hor…
View article: Lazy Heuristic Search for Solving POMDPs with Expensive-to-Compute Belief Transitions
Lazy Heuristic Search for Solving POMDPs with Expensive-to-Compute Belief Transitions Open
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 (POMDPs), which are typically formulated as belief MDPs. A bel…
View article: Real-Time LaCAM for Real-Time MAPF
Real-Time LaCAM for Real-Time MAPF Open
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 long and be impractical in real-world applications. Instead, r…
View article: Lazy Heuristic Search for Solving POMDPs with Expensive-to-Compute Belief Transitions
Lazy Heuristic Search for Solving POMDPs with Expensive-to-Compute Belief Transitions Open
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 (POMDPs), which are typically formulated as belief MDPs. A bel…
View article: MOSAIC: A Skill-Centric Algorithmic Framework for Long-Horizon Manipulation Planning
MOSAIC: A Skill-Centric Algorithmic Framework for Long-Horizon Manipulation Planning Open
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 novel tasks by flexibly composing generic skills. Solution…
View article: Windowed MAPF with Completeness Guarantees
Windowed MAPF with Completeness Guarantees Open
Traditional multi-agent path finding (MAPF) methods try to compute entire collision free start-goal paths, with several algorithms offering completeness guarantees. However, computing partial paths offers significant advantages including f…
View article: RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation
RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation Open
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 tasks. However, they often fail during deployment due to noisy…
View article: Implicit Graph Search for Planning on Graphs of Convex Sets
Implicit Graph Search for Planning on Graphs of Convex Sets Open
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 relationships within the decomposition, and then simultaneously…
View article: Multi-Robot Motion Planning with Diffusion Models
Multi-Robot Motion Planning with Diffusion Models Open
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 mostly been confined to single-robot and small-scale environm…
View article: Windowed MAPF with Completeness Guarantees
Windowed MAPF with Completeness Guarantees Open
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 systems where agents need to replan fast. Methods that addres…
View article: A POMDP-based hierarchical planning framework for manipulation under pose uncertainty
A POMDP-based hierarchical planning framework for manipulation under pose uncertainty Open
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 dark. In these cases, utilizing contacts to localize the targe…
View article: Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large Scale Imitation Learning for MAPF
Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large Scale Imitation Learning for MAPF Open
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 focused on developing high-performance heuristic search method…
View article: A Data Efficient Framework for Learning Local Heuristics
A Data Efficient Framework for Learning Local Heuristics Open
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 instead of learning the entire heuristic estimate, learns a …
View article: From Space-Time to Space-Order: Directly Planning a Temporal Planning Graph by Redefining CBS (Extended Abstract)
From Space-Time to Space-Order: Directly Planning a Temporal Planning Graph by Redefining CBS (Extended Abstract) Open
The majority of multi-agent path finding (MAPF) methods compute collision-free space-time paths which require agents to be at a specific location at a specific discretized timestep. However, executing these space-time paths directly on rob…
View article: Unconstraining Multi-Robot Manipulation: Enabling Arbitrary Constraints in ECBS with Bounded Sub-Optimality
Unconstraining Multi-Robot Manipulation: Enabling Arbitrary Constraints in ECBS with Bounded Sub-Optimality Open
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 Conflict-Based Search (CBS) algorithm have made large stri…
View article: Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences
Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences Open
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-dimensional composite state space renders many well-known motion p…
View article: MAPF in 3D Warehouses: Dataset and Analysis
MAPF in 3D Warehouses: Dataset and Analysis Open
Recent works have made significant progress in multi-agent path finding (MAPF), with modern methods being able to scale to hundreds of agents, handle unexpected delays, work in groups, etc. The vast majority of these methods have focused o…
View article: Improving Learnt Local MAPF Policies with Heuristic Search
Improving Learnt Local MAPF Policies with Heuristic Search Open
Multi-agent path finding (MAPF) is the problem of finding collision-free paths for a team of agents to reach their goal locations. State-of-the-art classical MAPF solvers typically employ heuristic search to find solutions for hundreds of …
View article: Unconstraining Multi-Robot Manipulation: Enabling Arbitrary Constraints in ECBS with Bounded Sub-Optimality
Unconstraining Multi-Robot Manipulation: Enabling Arbitrary Constraints in ECBS with Bounded Sub-Optimality Open
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 Conflict-Based Search (CBS) algorithm have made large stri…
View article: From Space-Time to Space-Order: Directly Planning a Temporal Planning Graph by Redefining CBS
From Space-Time to Space-Order: Directly Planning a Temporal Planning Graph by Redefining CBS Open
The majority of multi-agent path finding (MAPF) methods compute collision-free space-time paths which require agents to be at a specific location at a specific discretized timestep. However, executing these space-time paths directly on rob…
View article: A Data Efficient Framework for Learning Local Heuristics
A Data Efficient Framework for Learning Local Heuristics Open
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 instead of learning the entire heuristic estimate, learns a …
View article: Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences
Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences Open
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-dimensional composite state space renders many well-known motion p…
View article: Improving Learnt Local MAPF Policies with Heuristic Search
Improving Learnt Local MAPF Policies with Heuristic Search Open
Multi-agent path finding (MAPF) is the problem of finding collision-free paths for a team of agents to reach their goal locations. State-of-the-art classical MAPF solvers typically employ heuristic search to find solutions for hundreds of …
View article: PINSAT: Parallelized Interleaving of Graph Search and Trajectory Optimization for Kinodynamic Motion Planning
PINSAT: Parallelized Interleaving of Graph Search and Trajectory Optimization for Kinodynamic Motion Planning Open
Trajectory optimization is a widely used technique in robot motion planning for letting the dynamics and constraints on the system shape and synthesize complex behaviors. Several previous works have shown its benefits in high-dimensional c…
View article: Preprocessing-based Kinodynamic Motion Planning Framework for Intercepting Projectiles using a Robot Manipulator
Preprocessing-based Kinodynamic Motion Planning Framework for Intercepting Projectiles using a Robot Manipulator Open
We are interested in studying sports with robots and starting with the problem of intercepting a projectile moving toward a robot manipulator equipped with a shield. To successfully perform this task, the robot needs to (i) detect the inco…
View article: Constant-time Motion Planning with Anytime Refinement for Manipulation
Constant-time Motion Planning with Anytime Refinement for Manipulation Open
Robotic manipulators are essential for future autonomous systems, yet limited trust in their autonomy has confined them to rigid, task-specific systems. The intricate configuration space of manipulators, coupled with the challenges of obst…