SeeQ: Training Generalist Value Functions for Long-Horizon Robotic Manipulation
SeeQ enhances long-horizon robotic manipulation by learning Q-values for subtasks, significantly improving policy success rates.
Key Findings
Methodology
SeeQ learns subtask-elicited Q-values to shorten the value prediction horizon. It uses a vision-language model (VLM) backbone, pretrained on diverse robot manipulation data, and fine-tuned on downstream tasks. The model autoregressively predicts the active subtask in natural language before estimating its value.
Key Results
- In four real-world tasks, SeeQ improved the shirt-hang task success rate from 10/24 to 22/24, and lid-sealing from 10/24 to 15/24.
- For grocery-packing, the success rate increased from 9/24 to 17/24, indicating significant performance gains even with expert demonstrations.
- SeeQ improved the lego-disassembly task success rate from 5/24 to 10/24, showing its advantage in multi-stage tasks.
Significance
SeeQ addresses the challenge of learning Q-values in long-horizon tasks, particularly with sparse rewards and long credit-assignment horizons. By focusing on subtask-level value learning, SeeQ provides an effective method for policy optimization, significantly enhancing robot performance in complex tasks.
Technical Contribution
SeeQ introduces subtask-level Q-value learning, circumventing traditional difficulties in long-horizon tasks. By leveraging VLM's visual and language capabilities, SeeQ effectively learns and generalizes from diverse robot data.
Novelty
SeeQ is the first to apply subtask-level Q-value learning to long-horizon robotic tasks, significantly shortening the value learning horizon and reducing reliance on human annotations by predicting subtasks in natural language.
Limitations
- In complex tasks, subtask prediction may be inaccurate, affecting overall performance.
- Requires extensive pretraining data for effective generalization.
Future Work
Future work could explore more efficient subtask decomposition methods and applications in more complex multi-robot systems.
AI Executive Summary
In the field of robotic manipulation, long-horizon tasks pose a significant challenge due to their complexity and multi-stage nature. Traditional Q-value learning methods struggle with sparse rewards and long credit-assignment horizons.
SeeQ introduces subtask-elicited Q-value functions to shorten the value prediction horizon. Utilizing a vision-language model (VLM) backbone, SeeQ is pretrained on diverse robot manipulation data and fine-tuned on downstream tasks. By autoregressively predicting the active subtask in natural language and estimating its value, SeeQ significantly improves policy success rates across multiple real-world tasks.
Experimental results demonstrate SeeQ's outstanding performance in four long-horizon tasks, particularly those requiring precise manipulation and multi-stage planning. Despite challenges such as inaccurate subtask predictions, SeeQ offers a novel approach to policy optimization in long-horizon tasks.
Deep Analysis
Background
Long-horizon robotic manipulation tasks often involve multiple stages or require repeated attempts, where traditional imitation learning policies perform poorly. Q-value functions can improve these policies by ranking candidate actions or guiding policy improvement, but learning from sparse task-level rewards entails long credit-assignment horizons and broad data coverage.
Core Problem
The complexity of long-horizon tasks lies in their multi-stage nature and high precision requirements. Traditional Q-value learning methods struggle with sparse rewards and long credit-assignment horizons, making policy optimization challenging.
Innovation
SeeQ introduces subtask-elicited Q-value learning to shorten the value prediction horizon. By leveraging VLM's visual and language capabilities, SeeQ effectively learns and generalizes from diverse robot data.
Methodology
- �� Use VLM as a backbone for pretraining
- �� Autoregressively predict the active subtask
- �� Estimate the Q-value for the subtask
- �� Fine-tune the Q-value function on downstream tasks
Experiments
Experiments were conducted on four real-world long-horizon tasks using different datasets and baseline policies. The performance improvements of SeeQ were evaluated by comparing different Q-value learning methods.
Results
SeeQ significantly improved policy success rates across multiple tasks, particularly those requiring precise manipulation and multi-stage planning. The results demonstrate the effectiveness of subtask-level Q-value learning in addressing long-horizon challenges.
Applications
SeeQ can be applied to robotic tasks requiring multi-stage planning and precise manipulation, such as assembly, packaging, and complex object handling. Its ability to learn effectively from diverse data makes it suitable for different robotic platforms.
Limitations & Outlook
While SeeQ performs well across multiple tasks, it relies heavily on accurate subtask predictions and requires extensive pretraining data. Future work could explore more efficient subtask decomposition methods.
Plain Language Accessible to non-experts
Imagine a factory where workers need to complete a series of complex tasks. Traditional methods are like asking workers to complete the entire task without clear instructions, while SeeQ is like assigning specific subtasks to each worker with clear guidance. This way, workers can focus on completing their part, ultimately improving the factory's efficiency.
ELI14 Explained like you're 14
Imagine playing a complex game where you need to complete many tasks to win. Traditional methods are like trying to remember all the steps at once, while SeeQ is like getting hints for each small task, helping you complete them step by step. This makes it easier to win the game!
Glossary
Q-value function
A function used to evaluate the expected return of taking a specific action in a given state.
Used to guide policy improvement and action selection.
Subtask
A smaller part of a task with a clear objective and termination condition.
SeeQ optimizes policies through subtask-level Q-value learning.
Vision-Language Model (VLM)
A model that combines visual and language information to perform tasks.
SeeQ uses VLM as a backbone for pretraining.
Policy Optimization
The process of adjusting a policy to maximize expected returns.
SeeQ optimizes policies through subtask-level Q-value learning.
Long-Horizon Task
A complex task requiring multi-stage planning and precise manipulation.
SeeQ focuses on solving policy optimization problems in long-horizon tasks.
Open Questions Unanswered questions from this research
- 1 How to achieve SeeQ's effectiveness without extensive pretraining data?
- 2 How to improve subtask prediction accuracy to further enhance SeeQ's performance?
Applications
Immediate Applications
Industrial Robot Assembly
SeeQ can be used to improve the efficiency of industrial robots in complex assembly tasks, reducing error rates.
Long-term Vision
Smart Home Robots
In the future, SeeQ could be used to develop smart robots capable of autonomously completing complex household chores, enhancing quality of life.
Abstract
Despite rapid progress, generalist robot policies remain brittle on complex, long-horizon tasks that comprise multiple stages or require repeated attempts and deliberation on the same underlying stage before success. Q-value functions can improve these policies by ranking candidate actions or guiding policy improvement, but learning from sparse task-level rewards entails long credit-assignment horizons, difficult Bellman backups, and broad data-coverage requirements. We introduce SeeQ (Subtask-elicited Q-functions), which instead learns Q-values for the currently active subtask. This shortens the value-prediction horizon and enables effective learning with temporal-difference (TD) objectives. During training, subtask-level annotations present in offline robot data provide the decomposition and enable learning from broad, potentially suboptimal robot datasets. To eliminate the need for human annotations or modular subtask prediction systems at test time, our Q-function architecture is trained to autoregressively predict the active subtask in natural language before estimating its value. We instantiate SeeQ using a base vision-language backbone, pretrain it on diverse open-source robot manipulation data, and finetune it on downstream tasks. Across four real-world manipulation tasks on two bimanual robot platforms, the SeeQ value function substantially improves best-of-N policy steering.