arXiv subjects

Isaac Ngui

Publications and source records attributed to Isaac Ngui.

5 recordsLinked to original sources

arXiv2026

Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement

A fundamental challenge in multi-robot motion planning is achieving sufficient coordination to avoid inter-robot conflicts without incurring the large computational expense of searching the joint configuration space of the robot group. In this work, we present a method for multiple mobile robot motion planning that achieves an improvement in planning time up to an order of magnitude by leveraging the insight that we can use discrete search over a workspace decomposition to provide coordination between robots during planning. While prior work uses workspace topology to inform when coordination between robots is needed and then composes robots into their joint configuration space, we take a step further by iteratively refining our workspace representation to allow our planner to search smaller, decoupled configuration spaces.

cs.RO↗

arXiv2025

ERUPT: An Open Toolkit for Interfacing with Robot Motion Planners in Extended Reality

We propose the Extended Reality Universal Planning Toolkit (ERUPT), an extended reality (XR) system for interactive motion planning. Our system allows users to create and dynamically reconfigure environments while they plan robot paths. In immersive three-dimensional XR environments, users gain a greater spatial understanding. XR also unlocks a broader range of natural interaction capabilities, allowing users to grab and adjust objects in the environment similarly to the real world, rather than using a mouse and keyboard with the scene projected onto a two-dimensional computer screen. Our system integrates with MoveIt, a manipulation planning framework, allowing users to send motion planning requests and visualize the resulting robot paths in virtual or augmented reality. We provide a broad range of interaction modalities, allowing users to modify objects in the environment and interact with a virtual robot. Our system allows operators to visualize robot motions, ensuring desired behavior as it moves throughout the environment, without risk of collisions within a virtual space, and to then deploy planned paths on physical robots in the real world.

cs.RO↗

arXiv2025

Lazy-DaSH: Lazy Approach for Hypergraph-based Multi-robot Task and Motion Planning

We introduce Lazy-DaSH, an improvement over the recent state of the art multi-robot task and motion planning method DaSH, which scales to more than double the number of robots and objects compared to the original method and achieves an order of magnitude faster planning time when applied to a multi-manipulator object rearrangement problem. We achieve this improvement through a hierarchical approach, where a high-level task planning layer identifies planning spaces required for task completion, and motion feasibility is validated lazily only within these spaces. In contrast, DaSH precomputes the motion feasibility of all possible actions, resulting in higher costs for constructing state space representations. Lazy-DaSH maintains efficient query performance by utilizing a constraint feedback mechanism within its hierarchical structure, ensuring that motion feasibility is effectively conveyed to the query process. By maintaining smaller state space representations, our method significantly reduces both representation construction time and query time. We evaluate Lazy-DaSH in four distinct scenarios, demonstrating its scalability to increasing numbers of robots and objects, as well as its adaptability in resolving conflicts through the constraint feedback mechanism.

cs.RO↗

arXiv2024

Extended Reality System for Robotic Learning from Human Demonstration

Many real-world tasks are intuitive for a human to perform, but difficult to encode algorithmically when utilizing a robot to perform the tasks. In these scenarios, robotic systems can benefit from expert demonstrations to learn how to perform each task. In many settings, it may be difficult or unsafe to use a physical robot to provide these demonstrations, for example, considering cooking tasks such as slicing with a knife. Extended reality provides a natural setting for demonstrating robotic trajectories while bypassing safety concerns and providing a broader range of interaction modalities. We propose the Robot Action Demonstration in Extended Reality (RADER) system, a generic extended reality interface for learning from demonstration. We additionally present its application to an existing state-of-the-art learning from demonstration approach and show comparable results between demonstrations given on a physical robot and those given using our extended reality system.

cs.RO↗

arXiv2023

Scalable Multi-Robot Motion Planning Using Workspace Guidance-Informed Hypergraphs

In this work, we propose a method for multiple mobile robot motion planning that efficiently plans for robot teams up to 128 robots (an order of magnitude larger than existing state-of-the-art methods) in congested settings with narrow passages in the environment. We achieve this improvement in scalability by extending the state-of-the-art Decomposable State Space Hypergraph (DaSH) multi-robot planning framework to support mobile robot motion planning in congested environments. This is a problem that DaSH cannot be directly applied to because it lacks a highly structured, easily discretizable task space and features kinodynamic constraints. We accomplish this by exploiting knowledge about the workspace topology to limit exploration of the planning space and through modifying DaSH's conflict resolution scheme. This guidance captures when coordination between robots is necessary, allowing us to decompose the intractably large multi-robot search space while limiting risk of inter-robot conflicts by composing relevant robot groups together while planning.

cs.RO↗