Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL
arXiv:2602.12636v1 Announce Type: new Abstract: Designing suitable rewards poses a significant challenge in reinforcement learning (RL), especially for embodied manipulation. Trajectory success rewards are suitable for human judges or model fitting, but the sparsity severely limits RL sample efficiency. While recent methods have effectively improved RL via dense rewards, they rely heavily on high-quality human-annotated data or abundant expert supervision. To tackle these issues, this paper proposes Dual-granularity contrastive reward via generated Episodic Guidance (DEG), a novel framework to seek sample-efficient dense rewards without requiring human annotations or extensive supervision. Leveraging the prior knowledge of large video generation models, DEG only needs a small number of expert videos for domain adaptation to generate dedicated task guidance for each RL episode. Then, the proposed dual-granularity reward that balances coarse-grained exploration and fine-grained matching
arXiv:2602.12636v1 Announce Type: new Abstract: Designing suitable rewards poses a significant challenge in reinforcement learning (RL), especially for embodied manipulation. Trajectory success rewards are suitable for human judges or model fitting, but the sparsity severely limits RL sample efficiency. While recent methods have effectively improved RL via dense rewards, they rely heavily on high-quality human-annotated data or abundant expert supervision. To tackle these issues, this paper proposes Dual-granularity contrastive reward via generated Episodic Guidance (DEG), a novel framework to seek sample-efficient dense rewards without requiring human annotations or extensive supervision. Leveraging the prior knowledge of large video generation models, DEG only needs a small number of expert videos for domain adaptation to generate dedicated task guidance for each RL episode. Then, the proposed dual-granularity reward that balances coarse-grained exploration and fine-grained matching, will guide the agent to efficiently approximate the generated guidance video sequentially in the contrastive self-supervised latent space, and finally complete the target task. Extensive experiments on 18 diverse tasks across both simulation and real-world settings show that DEG can not only serve as an efficient exploration stimulus to help the agent quickly discover sparse success rewards, but also guide effective RL and stable policy convergence independently.
Executive Summary
The article introduces Dual-granularity contrastive reward via generated Episodic Guidance (DEG), a novel framework designed to enhance sample efficiency in reinforcement learning (RL), particularly for embodied manipulation tasks. DEG addresses the challenge of sparse trajectory success rewards by leveraging large video generation models to create dense, task-specific guidance without the need for extensive human annotation or supervision. The framework employs a dual-granularity reward system that balances coarse-grained exploration with fine-grained matching, guiding the agent through a contrastive self-supervised latent space to complete the target task efficiently. Experiments across 18 diverse tasks in both simulation and real-world settings demonstrate DEG's effectiveness in accelerating the discovery of sparse rewards and ensuring stable policy convergence.
Key Points
- ▸ DEG framework addresses the sparsity of trajectory success rewards in RL.
- ▸ Utilizes large video generation models for domain adaptation and task guidance.
- ▸ Dual-granularity reward system balances exploration and matching for efficient learning.
- ▸ Experiments show DEG's effectiveness across various tasks and settings.
Merits
Innovative Approach
DEG introduces a novel method for generating dense rewards using video generation models, reducing the reliance on human annotation and supervision.
Sample Efficiency
The framework significantly improves sample efficiency by providing detailed, task-specific guidance, accelerating the learning process.
Versatility
DEG demonstrates effectiveness across a wide range of tasks and settings, including both simulation and real-world environments.
Demerits
Dependency on Video Generation Models
The effectiveness of DEG is contingent on the quality and adaptability of the underlying video generation models, which may not be universally applicable or easily accessible.
Computational Resources
Generating and processing video guidance may require substantial computational resources, potentially limiting its deployment in resource-constrained environments.
Generalization to New Tasks
While DEG shows promise, its ability to generalize to entirely new tasks without additional expert videos or domain adaptation remains to be thoroughly tested.
Expert Commentary
The DEG framework represents a significant advancement in the field of reinforcement learning, particularly in addressing the long-standing challenge of sparse rewards. By leveraging video generation models to create dense, task-specific guidance, DEG not only enhances sample efficiency but also reduces the reliance on human annotation and supervision. The dual-granularity reward system is a particularly innovative aspect, as it effectively balances exploration and fine-grained matching, ensuring that the agent can navigate the learning process efficiently. The extensive experimental validation across diverse tasks and settings lends credibility to the framework's robustness and versatility. However, the dependency on video generation models and the potential computational overhead are notable limitations that warrant further investigation. Future research should focus on improving the generalizability of DEG to new tasks and exploring ways to mitigate computational demands. Overall, DEG holds promise for advancing the practical deployment of RL in real-world applications, particularly in robotics and automation.
Recommendations
- ✓ Further research should explore the integration of DEG with other advanced RL techniques to enhance its performance and applicability.
- ✓ Investigating the scalability of DEG to larger and more complex tasks could provide insights into its potential for broader industrial and commercial applications.