Multi Graph Search for High-Dimensional Robot Motion Planning

2026-02-12 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

Itamar Mishani and Maxim Likhachev introduce Multi-Graph Search (MGS), a novel search-based motion planning algorithm designed for high-dimensional robotic systems like manipulators and mobile manipulators. MGS generalizes classical unidirectional and bidirectional search to a multi-graph setting, addressing the challenge of generating efficient, consistent motions without excessive computational resources or memory. The algorithm incrementally expands multiple implicit graphs, concentrating exploration on high-potential regions and merging disconnected subgraphs through feasible transitions. MGS is proven to be complete and bounded-suboptimal, with empirical demonstrations of its effectiveness across various manipulation and mobile manipulation tasks. Code and benchmarks are available at https://multi-graph-search.github.io/.

Key takeaway

For AI Scientists developing motion planning solutions for complex robotic systems, MGS offers a robust alternative to traditional search algorithms. Its proven completeness and bounded suboptimality, coupled with efficient resource utilization, suggest it can improve real-time operation and reliability. You should explore integrating MGS into your planning frameworks, especially for high-dimensional manipulation and mobile robotics applications, to achieve more consistent and predictable robot motions.

Key insights

Multi-Graph Search (MGS) offers a complete and bounded-suboptimal approach for high-dimensional robot motion planning.

Principles

• Generalize classical search to multi-graph settings.
• Focus exploration on high-potential regions.
• Merge disconnected subgraphs incrementally.

Method

MGS incrementally expands multiple implicit graphs, focusing exploration on high-potential regions and merging subgraphs via feasible transitions to achieve complete and bounded-suboptimal motion planning.

In practice

• Apply MGS to high-dimensional manipulators.
• Utilize MGS for mobile manipulation tasks.

Topics

• Multi-Graph Search
• Robot Motion Planning
• High-Dimensional Robotics
• Manipulation Robotics

Code references

• LegendLeoChen/LEO-RobotAgent

Best for: AI Scientist, AI Researcher, Robotics Engineer, Research Scientist

Related on AIssential

• Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs — Scene graphs provide explicit, compact memory for robots to reason over long-horizon tasks under partial observability.
• Motion Planning of Cooperative Nonholonomic Mobile Manipulators — A two-step motion planning approach enables nonholonomic MMRs to cooperatively transport objects amidst complex obstacles.
• Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound — MAGR-BB efficiently infers multi-agent teams and goals from trajectories using a conditioned policy and factorized branch-and-bound search.
• Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning — Leveraging transfer learning and reward redesign significantly improves robotic motion planning efficiency.
• Why Robots Still Struggle With Simple Tasks (And What Might Finally Change That) | Karol Hausman, Co-Founder & CEO of Physical Intelligence — Combining internet-scale LLM knowledge with robot motion data enables general physical intelligence for complex tasks.
• Motion Planning in Compressed Representation Spaces — A generative framework unifies deep learning and model-based planning by searching a compressed, hierarchical latent space for efficient, flexible motion planning.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.