StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation
StageGuard learns stage transitions for long-horizon robot tasks via agentic distillation, significantly improving prediction accuracy.
Key Findings
Methodology
StageGuard employs an agentic distillation framework, using structured explanations generated by a teacher model to guide a lightweight student model's learning. The teacher model combines reasoning with demonstration trajectories to generate explanations for subtask completion and policy switching, which the student model uses for supervised fine-tuning.
Key Results
- On BEHAVIOR-1K, StageGuard achieved a stage transition prediction accuracy of 90.18%, significantly outperforming baseline models.
- On LIBERO, StageGuard achieved a transition completion rate of 96.23%, demonstrating its efficiency in simpler tasks.
- Ablation studies confirmed the importance of explanation learning in complex tasks.
Significance
StageGuard addresses the latency issues of existing methods in real-time monitoring by improving stage transition accuracy and efficiency. This framework is significant for both academia and industry, especially in complex robotic systems requiring efficient task planning.
Technical Contribution
StageGuard introduces an agentic distillation framework that combines structured explanations from a teacher model with self-explanations from a student model, providing a new method for efficient execution monitoring. It significantly reduces inference latency without sacrificing accuracy.
Novelty
StageGuard is the first to apply agentic distillation to learning stage transitions in robotic tasks, achieving efficient task monitoring through a combination of structured and self-explanations.
Limitations
- StageGuard relies on annotated demonstration data, which may limit performance in data-scarce scenarios.
- It is sensitive to partial observability, potentially leading to transition errors.
- It cannot compensate for low-level control failures.
Future Work
Future work may include more data-efficient adaptation methods, lower-cost supervision generation, active perception, and recovery policies.
AI Executive Summary
StageGuard is an innovative agentic distillation framework designed to enhance the accuracy of stage transitions in long-horizon robotic tasks. Existing methods often rely on pre-designed completion signal checkers, which are challenging to implement in real-world scenarios. StageGuard combines reasoning from a teacher model with demonstration trajectories to generate structured explanations for subtask completion and policy switching. The student model uses these explanations for supervised fine-tuning, enabling efficient online monitoring.
In experiments, StageGuard significantly improved stage transition prediction accuracy on the BEHAVIOR-1K and LIBERO benchmarks, demonstrating its superiority in complex tasks. Ablation studies confirmed the importance of explanation learning in complex tasks.
Despite significant advancements, StageGuard has limitations, such as dependency on annotated data and issues with partial observability. Future research will focus on improving data efficiency and developing more robust monitoring strategies.
Deep Analysis
Background
In long-horizon robotic tasks, determining when to terminate the current skill and move to the next subtask is crucial. Existing hierarchical planning frameworks often rely on pre-defined skill termination conditions, which are difficult to implement in real-world scenarios. Large-scale vision-language models (VLMs) offer strong reasoning capabilities, but their decision boundaries are not inherently aligned with task completion criteria, and cloud deployment introduces latency issues.
Core Problem
The core problem is how to make accurate and efficient stage transition decisions. Existing methods rely on pre-designed completion signal checkers, which are hard to implement in real-world scenarios. VLMs' reasoning latency and decision boundary inconsistencies also limit their application.
Innovation
StageGuard uses an agentic distillation framework that combines reasoning from a teacher model with demonstration trajectories to generate structured explanations for subtask completion and policy switching. The student model uses these explanations for supervised fine-tuning, enabling efficient online monitoring.
Methodology
- �� Use a teacher model to generate structured reasoning traces linking observations to ground-truth transition decisions.
- �� A student model learns these decision patterns through explanation learning, reducing inference costs.
- �� Evaluate on LIBERO and BEHAVIOR-1K to assess stage transition success.
Experiments
Experiments were conducted on LIBERO and BEHAVIOR-1K benchmarks, covering tabletop and mobile manipulation tasks. Each task used 10 trajectories for training, with remaining trajectories for evaluation. Qwen3.5-397B-A17B was used as the teacher model, and Qwen3.5-0.8B as the student model.
Results
On BEHAVIOR-1K, StageGuard achieved a stage transition prediction accuracy of 90.18%, and 96.23% on LIBERO. Ablation studies confirmed the importance of explanation learning in complex tasks.
Applications
StageGuard can be applied in complex robotic systems requiring efficient task planning, especially in real-time monitoring and task switching. Its efficient inference capabilities make it highly applicable in industrial automation.
Limitations & Outlook
StageGuard relies on annotated demonstration data, is sensitive to partial observability, and cannot compensate for low-level control failures. Future work will focus on improving data efficiency and developing more robust monitoring strategies.
Plain Language Accessible to non-experts
Imagine a factory where a robot's job is to move items from one place to another. StageGuard acts like a smart factory manager, knowing when to stop the robot's current task and start the next one. It observes the robot's actions and environment to determine if a task is complete and decides what to do next. This way, the robot doesn't waste time switching tasks too early or too late.
ELI14 Explained like you're 14
Imagine you're playing a super complex game with many levels, each with different tasks. StageGuard is like a super smart game assistant that tells you when to end the current level and move to the next. It watches your every move to make sure you're doing the right thing at the right time, so you can finish the game faster!
Glossary
Agentic Distillation
A method that uses structured explanations generated by a teacher model to guide a student model's learning.
Used to generate explanations for subtask completion and policy switching.
Vision-Language Model
A model that combines visual and language information for reasoning.
Used to observe and reason about task completion.
Stage Transition
The process of switching from one subtask to the next in a multi-stage task.
The core task of StageGuard.
LIBERO
A benchmark dataset for evaluating robotic tasks.
Used to validate StageGuard's stage transition prediction capabilities.
BEHAVIOR-1K
A benchmark dataset containing household mobile manipulation tasks.
Used to evaluate StageGuard's performance in complex tasks.
Open Questions Unanswered questions from this research
- 1 How to improve StageGuard's performance in data-scarce scenarios?
- 2 How to reduce StageGuard's dependency on annotated data?
- 3 How to enhance StageGuard's robustness in partially observable environments?
Applications
Immediate Applications
Industrial Automation
StageGuard can be used in industrial automation for real-time monitoring and task switching in complex tasks, improving production efficiency.
Long-term Vision
Smart Home Robots
StageGuard can be used in smart home robots to help them efficiently complete multi-stage tasks in complex environments.
Abstract
Hierarchical planning frameworks combine skills from multiple robot control policies for long-horizon task execution, where determining when to terminate the current skill and advance to the next subtask is essential. Existing approaches often rely on pre-designed completion signal checkers that are hard to obtain in real-world execution. Large-scale vision-language models (VLMs) offer strong reasoning capabilities, but their decision boundaries are not inherently aligned with task completion criteria, while cloud deployment and lengthy reasoning introduce substantial latency, limiting real-time monitoring. We propose StageGuard, an agentic distillation framework for accurate and efficient stage-transition decisions. StageGuard combines teacher-model reasoning with demonstration trajectories to generate structured explanations of subtask completion and policy switching. A lightweight student VLM uses these explanations to generate compact self-explanations, which are used for supervised fine-tuning. We evaluate stage-transition prediction on trajectories from two benchmarks and assess closed-loop task success through integration into hierarchical robot control on BEHAVIOR-1K, with further validation on real robots. Results show substantial improvements in stage-transition prediction while supporting efficient online monitoring.