Multi-Task Interactive Robot Fleet Learning with Visual World Models (2410.22689v1)

Abstract: Recent advancements in large-scale multi-task robot learning offer the potential for deploying robot fleets in household and industrial settings, enabling them to perform diverse tasks across various environments. However, AI-enabled robots often face challenges with generalization and robustness when exposed to real-world variability and uncertainty. We introduce Sirius-Fleet, a multi-task interactive robot fleet learning framework to address these challenges. Sirius-Fleet monitors robot performance during deployment and involves humans to correct the robot's actions when necessary. We employ a visual world model to predict the outcomes of future actions and build anomaly predictors to predict whether they will likely result in anomalies. As the robot autonomy improves, the anomaly predictors automatically adapt their prediction criteria, leading to fewer requests for human intervention and gradually reducing human workload over time. Evaluations on large-scale benchmarks demonstrate Sirius-Fleet's effectiveness in improving multi-task policy performance and monitoring accuracy. We demonstrate Sirius-Fleet's performance in both RoboCasa in simulation and Mutex in the real world, two diverse, large-scale multi-task benchmarks. More information is available on the project website: https://ut-austin-rpl.github.io/sirius-fleet

• The paper introduces SIRIUS-FLEET, a novel framework that integrates visual world models with adaptive anomaly prediction to boost multi-task robot fleet performance.
• The paper achieved a 13% improvement in simulation and a 45% boost in real-world deployment while maintaining over 95% success rates.
• The paper leverages human-robot interactive learning to gradually reduce human intervention, thereby enhancing overall system autonomy and efficiency.

Multi-Task Interactive Robot Fleet Learning with Visual World Models

The paper "Multi-Task Interactive Robot Fleet Learning with Visual World Models" presents a robust framework designed to tackle significant challenges in deploying multi-task robot fleets for household and industrial applications. These challenges consist primarily of the robots’ generalization and robustness when faced with real-world variability and uncertainties. The proposed framework, named SIRIUS-FLEET, introduces a novel approach that integrates a visual world model for runtime monitoring and enables human-robot interactive learning.

Overview

SIRIUS-FLEET provides a comprehensive structure integrating a multi-task policy with a runtime monitoring mechanism. The key components of this system include:

1. Visual World Model: A visual model that predicts future task outcomes by reconstructing past observations. This model is trained on diverse datasets and plays a crucial role in anomaly prediction across multiple tasks.
2. Runtime Monitoring: The framework employs anomaly predictors for real-time task supervision. These predictors, based on the visual world model embeddings, adaptively adjust their thresholds according to task performance metrics and human feedback. This adaptive threshold feature is crucial for maintaining a high level of robot autonomy while reducing the frequency of human interventions.
3. Human Interaction: By incorporating human oversight in the loop during early deployment stages, the system gradually reduces the need for human intervention as it learns and adapts, improving the robustness of the multi-task policy.

Key Findings

The paper demonstrates the effectiveness of SIRIUS-FLEET through extensive experimentation in both simulated environments and real-world scenarios. The framework's multi-task policy exhibited continual improvement over time, showing a 13% performance increase in simulations and a 45% increase in real-world deployment. Additionally, this system achieved an overall success rate exceeding 95%, highlighting its capability for consistent, reliable task execution. Notably, the Return of Human Effort (ROHE) also significantly improved, indicating more efficient human intervention dynamics.

Implications and Future Directions

From a theoretical standpoint, SIRIUS-FLEET represents a significant step towards scalable and adaptive robot fleet learning. It advances the discourse on leveraging visual world models for dynamic task monitoring and the role of human-robot interaction in enhancing system autonomy. Practically speaking, this framework promises substantial improvements in deploying autonomous systems in environments requiring complex, multi-task operations without extensive human involvement.

Potential future developments could involve extending the application of SIRIUS-FLEET to dynamic tasks, which may require more sophisticated modeling to handle temporal inconsistencies. Additionally, cross-embodiment learning could be explored to enhance the framework's generalizability across different types of robotic platforms.

Conclusion

In conclusion, SIRIUS-FLEET presents a methodologically sound and practically applicable framework for improving the performance and autonomy of multi-task robot fleets. By innovatively combining visual world models with adaptive anomaly prediction and human interaction, this framework sets a benchmark for future research and development in the field of autonomous robotics. Such systems hold the potential to revolutionize applications in diverse, unstructured real-world environments, contributing to the evolving landscape of advanced robotics.