SPARR: Simulation-based Policies with Asymmetric Real-world Residuals for Assembly
SPARR combines simulation-trained and real-world residual policies, improving assembly success by 38.4% and reducing cycle time by 29.7%.
Key Findings
Methodology
SPARR combines a simulation-trained base policy with a real-world residual policy. The base policy is trained using low-dimensional state observations and dense rewards, providing strong priors for initial behavior. The residual policy is learned in the real world using visual observations and sparse rewards, compensating for discrepancies in dynamics and sensor noise. This asymmetric design enables efficient adaptation to real-world variations.
Key Results
- SPARR achieves 95%-100% success rates across diverse two-part assembly tasks, improving success rates by 38.4% and reducing cycle time by 29.7% compared to state-of-the-art zero-shot sim-to-real methods.
- SPARR requires no human expertise, significantly outperforming real-world RL methods that rely heavily on human supervision.
- In experiments, SPARR consistently outperformed other methods, with significant improvements in success rate and efficiency across 10 tasks.
Significance
SPARR is significant in the field of robotic assembly, addressing the sim-to-real gap without requiring human supervision. It enhances success rates and efficiency in assembly tasks, making it suitable for industrial and manufacturing deployment. By combining simulation and real-world strategies, SPARR offers an efficient and scalable solution.
Technical Contribution
SPARR's technical contribution lies in its asymmetric residual learning framework, combining a simulation-trained state-based base policy with a vision-conditioned residual policy in the real world. This design leverages the efficiency of simulation training while enabling robust adaptation to real-world variations.
Novelty
SPARR is the first to apply an asymmetric residual learning framework to robotic assembly, combining simulation and real-world strategies to enhance success rates. Compared to existing methods, SPARR achieves efficient real-world adaptation without human supervision.
Limitations
- SPARR may perform poorly under extreme environmental changes, such as significant lighting variations or object appearance changes.
- Requires high-quality visual input, which may demand precise camera setup and calibration.
Future Work
Future work could explore SPARR's application in more complex assembly tasks and its potential in multi-robot collaboration. Further optimizing the robustness of visual inputs and reducing hardware dependency are also important directions.
AI Executive Summary
Robotic assembly tasks have long been challenging due to the need for precise, contact-rich manipulation. Existing simulation-based learning methods perform well in simulated environments but often falter in real-world settings due to the sim-to-real gap. Conversely, real-world reinforcement learning methods avoid this gap but heavily rely on human supervision and lack generalization to environmental changes.
SPARR is an innovative hybrid approach that combines a simulation-trained base policy with a real-world residual policy to efficiently adapt to real-world variations. The base policy is trained in simulation using low-dimensional state observations and dense rewards, providing strong priors for initial behavior. The residual policy is learned in the real world using visual observations and sparse rewards, compensating for discrepancies in dynamics and sensor noise. Experiments demonstrate that SPARR achieves near-perfect success rates across diverse two-part assembly tasks.
Compared to state-of-the-art zero-shot sim-to-real methods, SPARR improves success rates by 38.4% and reduces cycle time by 29.7%. Moreover, SPARR requires no human expertise, significantly outperforming real-world RL methods that rely heavily on human supervision. This makes SPARR highly promising for industrial and manufacturing deployment.
Deep Analysis
Background
Robotic assembly is a core challenge in robot learning, demanding high-precision, contact-rich manipulation. Simulation and sim-to-real transfer have emerged as powerful strategies for addressing these difficulties. Recent advances in simulation-based learning have led to the development of assembly policies that demonstrate strong performance in both simulated and real-world environments. However, current performance is insufficient for industrial deployment, where success rates of 95% or higher are typically required.
Core Problem
The core problem is the sim-to-real gap. Zero-shot performance of state-based policies can degrade significantly due to mismatches in physical parameters, camera calibration errors, state estimation noise, and variations in grasp pose. Vision-based policies are especially sensitive to visual domain shifts, such as changes in lighting, object appearance, or background, which can severely impair their real-world generalization.
Innovation
SPARR's core innovation lies in its asymmetric residual learning framework. The base policy is trained in simulation, providing strong priors and safe early exploration. The residual policy is learned in the real world, correcting for discrepancies in physical properties, state estimation errors, and visual or environmental differences. This design enables efficient adaptation to real-world environments without reliance on human supervision.
Methodology
- �� Pre-train a base policy in simulation using low-dimensional state observations and dense rewards.
- �� Collect demonstrations of the base policy in the real world.
- �� Train a residual policy in the real world using visual observations and sparse rewards.
- �� Combine the base and residual policies to execute a combined policy that adapts to real-world variations.
Experiments
Experiments involved 10 real-world robotic assembly tasks from the AutoMate dataset. The base policy was pre-trained in Isaac Lab using 128 parallel environments, completing 25 million environment steps. Various methods, including SERL, AutoMate, and SPARR, were compared under a 0.5-hour training budget.
Results
SPARR achieves 95%-100% success rates across diverse two-part assembly tasks, improving success rates by 38.4% and reducing cycle time by 29.7% compared to state-of-the-art zero-shot sim-to-real methods. SPARR requires no human expertise, significantly outperforming real-world RL methods that rely heavily on human supervision.
Applications
SPARR is applicable to industrial and manufacturing robotic assembly tasks, particularly in scenarios requiring high success rates and efficiency. Its lack of human supervision makes it highly promising for large-scale deployment.
Limitations & Outlook
SPARR may perform poorly under extreme environmental changes, such as significant lighting variations or object appearance changes. Additionally, SPARR requires high-quality visual input, which may demand precise camera setup and calibration. Future work could explore its application in more complex assembly tasks and its potential in multi-robot collaboration.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. You have a recipe (the simulation-trained base policy) that tells you how to make a dish, but sometimes the quality of ingredients or the kitchen environment varies (the sim-to-real gap). To ensure the dish tastes the same every time, you need to adjust based on the actual situation (the real-world residual policy). SPARR is like a smart chef who can quickly adjust the recipe based on the situation, ensuring a perfect dish every time.
ELI14 Explained like you're 14
Imagine you're playing a puzzle game. You have a guide (the simulation-trained base policy) that tells you how to complete the pattern. But sometimes, the puzzle pieces might be a bit off (the sim-to-real gap). To make sure you finish the puzzle, you need to adjust based on the actual situation (the real-world residual policy). SPARR is like a smart player who can quickly adjust the strategy based on the situation, ensuring the puzzle is completed every time!
Glossary
Sim-to-Real Gap
The phenomenon where policies trained in simulation perform poorly in the real world.
In the paper, the sim-to-real gap is the core issue SPARR aims to address.
Residual Policy
A policy that adjusts the base policy to compensate for differences between simulation and reality.
SPARR uses a residual policy to adapt to real-world variations.
Dense Reward
In reinforcement learning, feedback provided at each time step.
The base policy is trained in simulation using dense rewards.
Sparse Reward
In reinforcement learning, feedback provided only under specific conditions.
The residual policy is learned in the real world using sparse rewards.
Visual Observation
Information about the environment obtained through cameras, used for policy decision-making.
The residual policy uses visual observations to compensate for discrepancies in dynamics and sensor noise.
Open Questions Unanswered questions from this research
- 1 How to improve SPARR's robustness under extreme environmental changes?
- 2 How to reduce SPARR's dependency on the quality of visual inputs?
Applications
Immediate Applications
Industrial Robotic Assembly
SPARR can be used to enhance the success rate and efficiency of industrial robotic assembly tasks, especially in scenarios requiring high precision.
Long-term Vision
Multi-Robot Collaboration
SPARR has great potential in multi-robot collaboration, and future exploration of its performance in complex assembly tasks is promising.
Abstract
Robotic assembly presents a long-standing challenge due to its requirement for precise, contact-rich manipulation. While simulation-based learning has enabled the development of robust assembly policies, their performance often degrades when deployed in real-world settings due to the sim-to-real gap. Conversely, real-world reinforcement learning (RL) methods avoid the sim-to-real gap, but rely heavily on human supervision and lack generalization ability to environmental changes. In this work, we propose a hybrid approach that combines a simulation-trained base policy with a real-world residual policy to efficiently adapt to real-world variations. The base policy, trained in simulation using low-level state observations and dense rewards, provides strong priors for initial behavior. The residual policy, learned in the real world using visual observations and sparse rewards, compensates for discrepancies in dynamics and sensor noise. Extensive real-world experiments demonstrate that our method, SPARR, achieves near-perfect success rates across diverse two-part assembly tasks. Compared to the state-of-the-art zero-shot sim-to-real methods, SPARR improves success rates by 38.4% while reducing cycle time by 29.7%. Moreover, SPARR requires no human expertise, in contrast to the state-of-the-art real-world RL approaches that depend heavily on human supervision.