ARM: Advantage Reward Modeling for Long-Horizon Manipulation
Summary: arXiv:2604.03037v1 Announce Type: cross
Abstract
Long-horizon robotic manipulation remains challenging for reinforcement learning (RL) due to sparse rewards that provide limited guidance for credit assignment. Practical policy improvement relies heavily on richer intermediate supervision, such as dense progress rewards. However, these dense rewards are often costly to obtain and can be ill-suited to non-monotonic behaviors like backtracking and recovery.
Introduction
In recent years, the field of reinforcement learning has made significant strides in various applications, yet long-horizon robotic tasks continue to pose substantial challenges. The difficulty largely arises from the sparse nature of rewards in these complex tasks, which complicates the process of credit assignment. As a result, researchers are exploring alternative methods to enhance the reward modeling process.
Advantage Reward Modeling (ARM)
To address the shortcomings of traditional reward modeling, we propose the Advantage Reward Modeling (ARM) framework. This innovative approach shifts focus from hard-to-quantify absolute progress to estimating relative advantage. By doing so, ARM provides a more effective means of guiding reinforcement learning agents through complex tasks.
Tri-State Labeling Strategy
A key component of ARM is its cost-effective tri-state labeling strategy, which classifies progress into three categories:
- Progressive: Actions that lead to forward movement in task completion.
- Regressive: Actions that detract from task progress.
- Stagnant: Actions that neither advance nor regress the task.
This classification reduces human cognitive overhead while ensuring high cross-annotator consistency. By training on these intuitive signals, ARM facilitates automated progress annotation for both complete demonstrations and fragmented data obtained through DAgger-style approaches.
Integration with Offline RL Pipeline
Incorporating ARM into an offline reinforcement learning pipeline allows for adaptive action-reward reweighting. This effectively filters out suboptimal samples, enhancing the overall training process and improving the agent’s performance. As a result, ARM demonstrates a significant advantage in data efficiency and stability compared to existing Variable-Length Action (VLA) baselines.
Results and Conclusion
Our experiments reveal that the ARM framework achieves an impressive 99.4% success rate on a challenging long-horizon towel-folding task. This success underscores the potential of ARM to revolutionize the landscape of long-horizon robotic manipulation. Furthermore, the approach requires near-zero human intervention during policy training, making it a practical solution for real-world applications.
In conclusion, Advantage Reward Modeling presents a promising alternative to traditional reward structures in reinforcement learning. By shifting the focus from absolute progress to relative advantage and employing an efficient labeling strategy, ARM paves the way for more effective long-horizon manipulation strategies in robotics.