MOTION-PLANNINGCURRENT2026-04-22

Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems

Jiabao Ji, Yongchao Chen, Yang Zhang, Ramana Rao Kompella, Chuchu Fan, Gaowen Liu, Shiyu Chang

This paper solves multi-robot coordination in cluttered spaces by jointly learning Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. Control & PlanningPlanningFiguring out what the robot should do before or during movement. (what robots should do) and Control & PlanningMotion planningFinding a path or motion that gets the robot from start to goal. (how they move) using waypoints as an intermediate representation. The key insight is using curriculum learning to propagate collision/feasibility Control & PlanningFeedbackInformation returned from sensors during action to help correct behavior. upward from the motion planner to the Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. planner, enabling teams of 9+ robots to navigate dense obstacles without human-engineered planners.

THE PROBLEM

This paper focuses on Control & PlanningMotion planningFinding a path or motion that gets the robot from start to goal.. This paper solves multi-robot coordination in cluttered spaces by jointly learning Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. Control & PlanningPlanningFiguring out what the robot should do before or during movement. (what robots should do) and Control & PlanningMotion planningFinding a path or motion that gets the robot from start to goal. (how they move) using waypoints as an intermediate representation. The key insight is using curriculum learning to propagate collision/feasibility Control & PlanningFeedbackInformation returned from sensors during action to help correct behavior. upward from the motion planner to the Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. planner, enabling teams of 9+ robots to navigate dense obstacles without human-engineered planners. Read the paper by tracking the Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. definition, the Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. or data assumptions, and the evidence that supports the claimed improvement.

HOW IT WORKS

1

Task framing

The paper frames the work as Control & PlanningMotion planningFinding a path or motion that gets the robot from start to goal.. Start here because it defines what success means and which assumptions the rest of the method inherits.

2

Core method

This paper solves multi-robot coordination in cluttered spaces by jointly learning Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. Control & PlanningPlanningFiguring out what the robot should do before or during movement. (what robots should do) and Control & PlanningMotion planningFinding a path or motion that gets the robot from start to goal. (how they move) using waypoints as an intermediate representation. The key insight is using curriculum learning to propagate collision/feasibility Control & PlanningFeedbackInformation returned from sensors during action to help correct behavior. upward from the motion planner to the Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. planner, enabling teams of 9+ robots to navigate dense obstacles without human-engineered planners. When reading the method section, identify the inputs, the learned or engineered representation, and the Core ConceptsActionA command the robot sends to its motors, controller, or low-level system. or prediction produced by the system.

3

Data and supervision

For robotics work, the data story is part of the method: check whether the system depends on Imitation & Reinforcement LearningTeleoperation (teleop)A human remotely controlling the robot, often to collect demonstrations., Simulation & Sim-to-RealSimulationA virtual environment where robots can be trained or tested., internet video, human labels, or Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. rollouts.

4

Evaluation evidence

The paper should be judged through its Simulation & Sim-to-RealEvaluationMeasuring how well a robot system performs. protocol: what data is used, what Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. or simulator is tested, and which Evaluation & ResearchBaselineA reference method used for comparison. comparisons support the claim. Look for the gap between the headline result and the Simulation & Sim-to-RealDeploymentPutting the trained system on a real robot. setting you would actually care about.

FIGURES

KEY RESULTS

Main contributionConceptual contribution

This paper solves multi-robot coordination in cluttered spaces by jointly learning Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. Control & PlanningPlanningFiguring out what the robot should do before or during movement. (what robots should do) and Control & PlanningMotion planningFinding a path or motion that gets the robot from start to goal. (how they move) using waypoints as an intermediate representation. The key insight is using curriculum learning to propagate collision/feasibility Control & PlanningFeedbackInformation returned from sensors during action to help correct behavior. upward from the motion planner to the Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. planner, enabling teams of 9+ robots to navigate dense obstacles without human-engineered planners.

WHY DEVELOPERS SHOULD CARE

This paper solves multi-robot coordination in cluttered spaces by jointly learning Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. Control & PlanningPlanningFiguring out what the robot should do before or during movement. (what robots should do) and Control & PlanningMotion planningFinding a path or motion that gets the robot from start to goal. (how they move) using waypoints as an intermediate representation. The key insight is using curriculum learning to propagate collision/feasibility Control & PlanningFeedbackInformation returned from sensors during action to help correct behavior. upward from the motion planner to the Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. planner, enabling teams of 9+ robots to navigate dense obstacles without human-engineered planners.

LIMITATIONS

The main limitation to check is whether the claimed behavior holds outside the paper's reported setup. That means testing across different Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. embodiments, scenes, objects, and data distributions.

WHAT COMES NEXT

The practical next step is independent reproduction with clear baselines, ablations, and stress tests. For a developer, the useful follow-up is to map the paper's Control & PlanningMotion planningFinding a path or motion that gets the robot from start to goal. assumptions onto a concrete Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. stack, then test the smallest version of the method that could run end to end.

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