Grasp-Then-Plan with Failure Attribution: A Closed Two-Stage Framework for Precise and Generalizable Robotic Manipulation
GTP-FA framework improves robotic manipulation success rates by 30% through failure attribution.
Key Findings
Methodology
The GTP-FA framework optimizes grasping and planning modules through a failure attribution model. It generates grasp candidates and performs downstream planning based on selected grasps. The failure attribution model generalizes to unseen grasps and provides a stable distribution over failure modes for optimization. Task-level priors and risk penalties are injected into grasp scoring to suppress unstable grasps; planning targets high-risk states through data collection and fine-tuning.
Key Results
- Experiments show GTP-FA improves task success rates by 30% across RL, IL, diffusion-policy, and VLA settings.
- In both simulation and real-robot experiments, GTP-FA significantly outperforms baseline methods.
- The failure attribution model effectively identifies high-risk states, optimizing the planning process.
Significance
The GTP-FA framework enhances robotic manipulation efficiency by addressing the challenge of failure source identification in long-horizon tasks. Its impact is significant in academia and industry, particularly in reducing trial-and-error time in complex tasks.
Technical Contribution
GTP-FA offers a novel failure attribution model and task-driven optimization strategies, achieving higher success rates and stability compared to existing methods. It provides new theoretical guarantees and engineering possibilities for robotic manipulation.
Novelty
GTP-FA is the first framework to integrate failure attribution for grasp and planning optimization, offering more precise failure identification and optimization strategies compared to traditional methods.
Limitations
- Failure attribution may be inaccurate in extremely complex tasks.
- Requires substantial data for model training.
Future Work
Future work could extend the failure attribution model to more task scenarios and optimize data collection strategies to reduce training time.
AI Executive Summary
In robotic manipulation, the tight coupling between grasping and motion planning often obscures the true source of failure, leading to inefficient trial-and-error. Existing methods fall short in identifying failure sources in long-horizon tasks.
The GTP-FA framework optimizes grasping and planning modules through a failure attribution model, significantly improving task success rates. It generates grasp candidates and performs downstream planning based on selected grasps. The failure attribution model generalizes to unseen grasps and provides a stable distribution over failure modes for optimization. Task-level priors and risk penalties are injected into grasp scoring to suppress unstable grasps; planning targets high-risk states through data collection and fine-tuning.
Experiments show GTP-FA improves task success rates by 30% across RL, IL, diffusion-policy, and VLA settings. Its impact is significant in academia and industry, particularly in reducing trial-and-error time in complex tasks. Future work could extend the failure attribution model to more task scenarios and optimize data collection strategies to reduce training time.
Deep Analysis
Background
The field of robotic manipulation has evolved from simple grasping tasks to complex motion planning. Traditional methods like RL and IL struggle with long-horizon tasks, failing to identify sources of failure. Existing research often focuses on optimizing individual modules without considering the interplay between grasping and planning.
Core Problem
The tight coupling between grasping and planning obscures failure sources, leading to inefficient trial-and-error. Identifying and optimizing failure sources in long-horizon tasks is a critical challenge in robotic manipulation.
Innovation
The GTP-FA framework optimizes grasping and planning modules through a failure attribution model. The model generalizes to unseen grasps and provides a stable distribution over failure modes for optimization. Task-level priors and risk penalties are injected into grasp scoring to suppress unstable grasps; planning targets high-risk states through data collection and fine-tuning.
Methodology
- �� Generate grasp candidates
- �� Perform downstream planning based on selected grasps
- �� Failure attribution model identifies failure sources
- �� Inject task-level priors and risk penalties into grasp scoring
- �� Target high-risk states through data collection and fine-tuning
Experiments
Experiments conducted in both simulation and real-robot settings using RL, IL, diffusion-policy, and VLA configurations. Design includes identifying and optimizing failure modes across different task scenarios.
Results
Experiments show GTP-FA improves task success rates by 30% across RL, IL, diffusion-policy, and VLA settings. The failure attribution model effectively identifies high-risk states, optimizing the planning process.
Applications
The GTP-FA framework is applicable to complex robotic manipulation tasks, particularly in industrial automation and smart manufacturing.
Limitations & Outlook
Failure attribution may be inaccurate in extremely complex tasks. Requires substantial data for model training. Future work could extend the failure attribution model to more task scenarios and optimize data collection strategies to reduce training time.
Plain Language Accessible to non-experts
Imagine a kitchen with a robot assistant that needs to grasp and move items. Traditional methods are like having the robot repeatedly try different ways until it succeeds. GTP-FA is like giving the robot a smart assistant that analyzes why it failed and tells the robot how to improve. This way, the robot completes tasks faster and more accurately.
ELI14 Explained like you're 14
Imagine you're playing a grab-and-place game where the robot needs to pick up items and put them in the right spot. Regular robots keep trying until they get it right. But GTP-FA is like a smart game assistant that tells the robot what's wrong and how to fix it. This way, the robot finishes tasks faster and scores higher!
Glossary
Failure Attribution Model
A model that identifies and analyzes failure causes to optimize robotic operations.
Used to identify failure sources in grasping and planning.
Grasp Candidates
The grasping methods selected by the robot before operation.
Used as the basis for generating grasp and planning.
Task-Level Priors
Ranking of grasping methods based on task importance.
Used to optimize the grasping module.
Risk Penalties
Penalties for unstable or incompatible grasping methods.
Used to suppress unstable grasps.
Data Collection
Collecting operational data to optimize the planning process.
Used to identify high-risk states.
Open Questions Unanswered questions from this research
- 1 Accuracy of failure attribution in extremely complex tasks needs improvement.
- 2 Optimization of data collection strategies requires further research.
Applications
Immediate Applications
Industrial Automation
GTP-FA can be used to enhance efficiency and precision in automated production lines.
Long-term Vision
Smart Manufacturing
GTP-FA can drive the development of smart manufacturing, enabling more complex robotic operations.
Abstract
In robotic manipulation, the tight coupling between grasping and motion planning often obscures the true source of failure, leading to inefficient trial-and-error. To enable efficient long-horizon manipulation, we propose GTP-FA (Grasp-Then-Plan with Failure Attribution), a task-oriented two-stage grasp-then-plan framework that generates grasp candidates and performs downstream motion planning conditioned on the selected grasp. Given a failed manipulation trajectory, we learn a failure attribution model that generalizes to unseen grasps and produces a stable distribution over failure modes for diagnosis-guided optimization. Based on these attribution results, we then optimize both modules in a diagnosis-driven manner: on the grasping side, we inject task-level priors and risk penalties into grasp candidate scoring and optimization to suppress unstable or task-incompatible grasps; on the planning side, we target high-risk initial states through data collection and fine-tuning to address genuine planning bottlenecks. We evaluate the proposed framework in both simulation and real-robot experiments, and show that GTP-FA improves the corresponding base learners across RL, IL, diffusion-policy, and VLA-based settings, achieving substantially higher overall task success rates.