Enterprise AI Analysis
Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams
Scale-Plan addresses the critical challenge of long-horizon task planning for heterogeneous multi-robot systems by efficiently filtering task-irrelevant information and leveraging structured LLM reasoning.
Executive Impact: Unlocking Multi-Robot Efficiency
Scale-Plan delivers significant improvements in task completion rate, goal condition recall, and executability rate for complex multi-robot tasks by integrating action-graph-based filtering with a structured LLM planning pipeline.
25%
Overall TCR Improvement over LLM-Corrected
98%
Simple Task Executability Rate
16%
GCR Improvement on Complex Tasks
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology Overview
Performance & Results
Challenges & Limitations
Scale-Plan's innovative approach combines action-graph-based environment filtering with a structured LLM pipeline for robust and scalable multi-robot task planning.
Scale-Plan Enterprise Process Flow
Natural Language Task
→
Action-Graph Filtering
→
Task Decomposition
→
Robot Allocation
→
Plan Integration
→
AI2-THOR Execution
PDDL Domain Structure
Action graphs constructed offline to capture logical dependencies.
Task-Relevant Filtering: Scale-Plan constructs an action graph offline from the PDDL domain, where nodes are parameterized actions and edges encode dependencies. At runtime, shallow LLM reasoning guides a graph search to identify minimal task-relevant actions and objects, significantly reducing combinatorial complexity.
Evaluated on MAT2-THOR, Scale-Plan consistently outperforms existing LLM and hybrid baselines, demonstrating superior reliability and scalability across various task complexities.
| Method | Simple TCR (%) | Complex TCR (%) | Vague TCR (%) | Overall TCR (%) |
|---|---|---|---|---|
| LLM as a Planner | 44 | 18 | 57 | 37 |
| LLM+P (PDDL based) | 48 | 24 | 52 | 39 |
| LaMMA-P (PDDL plan only) | 36 | 18 | 57 | 33 |
| LaMMA-P (LLM corrected) | 76 | 24 | 50 | 69 |
| Scale-Plan (Ours) | 92 | 59 | 71 | 85 |
Scale-Plan consistently achieves the highest Task Completion Rate across all task categories, highlighting its robust filtering and structured reasoning.
92%
Task Completion Rate on Simple Tasks achieved by Scale-Plan.
Ablation Study Insights: Our ablation study showed that environment filtering (EF) and the structured planning pipeline are crucial for Scale-Plan's superior performance. The full model combining action-graph-based EF with task decomposition, allocation, and plan integration significantly outperforms variants without these components.
Despite its strengths, Scale-Plan faces challenges with environmental grounding and linguistic ambiguity, areas targeted for future enhancements.
Common Failure Modes
- Object Localization: LLM hallucinations can lead robots to navigate to incorrect object locations.
- Manipulation Constraints: Robots attempting to pick up new objects without releasing currently held ones.
- Affordance Reasoning: Failure to open receptacles before placing objects inside.
These issues underscore the need for integrating structured semantic knowledge and constraint-aware validation into LLM-based planning frameworks for enhanced reliability.
Future Work: Future efforts will focus on incorporating structured knowledge graphs to provide stronger environmental grounding, enabling a more reliable intermediate PDDL problem generation stage. This will improve the accuracy and consistency of generated PDDL formulations and allow classical planners to produce more robust and efficient multi-agent plans.
Advanced ROI Calculator
Estimate your potential efficiency gains and cost savings by deploying Scale-Plan in your enterprise.
Annual Cost Savings
$0
Annual Hours Reclaimed
0
Your Implementation Roadmap
A phased approach to integrate Scale-Plan into your existing multi-robot infrastructure and workflows.
Phase 1: Assessment & Customization (2-4 Weeks)
Initial discovery workshops, PDDL domain analysis, and identification of key task categories. Customization of action graph generation and LLM prompt engineering for your specific robotic capabilities and environment constraints.
Phase 2: Pilot Deployment & Validation (4-8 Weeks)
Deployment of Scale-Plan in a controlled environment or simulation (e.g., MAT2-THOR-based). Validation of plan generation, task completion rates, and error handling for critical multi-robot tasks.
Phase 3: Scaled Integration & Optimization (6-12 Weeks)
Full-scale integration into your operational multi-robot systems. Ongoing monitoring, performance optimization, and refinement of LLM grounding and failure recovery mechanisms for continuous improvement.
Ready to Scale Your Multi-Robot Operations?
Connect with our AI specialists to explore how Scale-Plan can transform your enterprise's autonomous capabilities.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat