Uncertainty-Aware Shared Autonomy System with Hierarchical Conservative Skill Inference (2312.02488v1)

Abstract: Shared autonomy imitation learning, in which robots share workspace with humans for learning, enables correct actions in unvisited states and the effective resolution of compounding errors through expert's corrections. However, it demands continuous human attention and supervision to lead the demonstrations, without considering the risks associated with human judgment errors and delayed interventions. This can potentially lead to high levels of fatigue for the demonstrator and the additional errors. In this work, we propose an uncertainty-aware shared autonomy system that enables the robot to infer conservative task skills considering environmental uncertainties and learning from expert demonstrations and corrections. To enhance generalization and scalability, we introduce a hierarchical structure-based skill uncertainty inference framework operating at more abstract levels. We apply this to robot motion to promote a more stable interaction. Although shared autonomy systems have demonstrated high-level results in recent research and play a critical role, specific system design details have remained elusive. This paper provides a detailed design proposal for a shared autonomy system considering various robot configurations. Furthermore, we experimentally demonstrate the system's capability to learn operational skills, even in dynamic environments with interference, through pouring and pick-and-place tasks. Our code will be released soon.

• The paper presents a hierarchical skill network that conservatively infers robot actions by modeling uncertainty with Monte-Carlo dropout.
• It applies uncertainty-aware methods to modulate action safety, achieving up to 90% success in tasks like pouring and pick-and-place.
• The system features a replicable framework with VR-based teleoperation and plans for public code release, enhancing practical applications and future research.

Uncertainty-Aware Shared Autonomy System with Hierarchical Conservative Skill Inference

The paper presents a noteworthy advancement in the domain of shared autonomy imitation learning, wherein a novel uncertainty-aware shared autonomy system is introduced. This system allows a robotic agent to infer task skills conservatively while considering environmental uncertainties and learning from human demonstrations and corrections. The primary objective is to mitigate the demands for continuous human intervention and reduce potential errors induced by human judgment, which can contribute to operator fatigue and overall inefficiency.

Key Contributions

1. Hierarchical Skill Network (HSN): The authors propose a hierarchical skill network (HSN) for skill inference and execution. This model draws inspiration from SPiRL architecture and implements a hierarchical structure to infer skill embeddings from environmental observations and decode these into robot actions. This structure facilitates the learning and scalability by operating at abstract levels, thus promoting a more generalizable and stable interaction framework in dynamic environments.
2. Uncertainty-Aware Conservative Skill Inference: A significant innovation is the application of Monte-Carlo dropout to the skill prior network for inferring skill uncertainty. This allows for conservative skill planning, where the conservatism modulation is proportional to the inferred uncertainty level. The conservative execution helps in averting risky actions and enhancing task success rates, particularly in dynamic environments with unpredictable variations.
3. Experimental Validation: The experiments demonstrated the system's competence in learning and executing complex manipulation tasks such as pouring and pick-and-place, both in static and dynamically changing environments. The system achieved 90% and 80% success rates in disturbance-free trials for pouring and pick-and-place tasks, respectively. With disruptions, success rates were slightly reduced but still notable at 80% for pouring and 70% for pick-and-place.
4. Practical Implementation and Public Availability: The paper details the system's design, including VR-based teleoperation interface, MPU (main processing unit), RCU (robot control unit), and the complete data communication framework. The authors also plan to release the source code, thus contributing to the replicability and long-term utility of their research within the robotics community.

Implications

Practical Implications:

The proposed system is poised to significantly impact practical applications involving robotic manipulation in dynamic environments. By incorporating uncertainty into the skill inference process, the system can operate more reliably with reduced demand for continuous human oversight. This can be particularly beneficial in industrial settings where robots are deployed for complex tasks such as assembly, material handling, and servicing, thus enhancing operational safety and efficiency.

Theoretical Implications:

From a theoretical perspective, the introduction of hierarchical skill networks augmented with uncertainty modeling represents a significant step forward. It provides a robust framework for addressing the gap between simulated and real-world dataset scarcity, and effectively manages the domain adaptation issues. Additionally, this approach advances the understanding of shared autonomy systems in terms of hierarchical learning and uncertainty quantification.

Future Developments

Looking ahead, several avenues for future research and development are apparent:

1. Extended Multi-Skill Learning: Future work could explore the integration of additional manipulation tasks, thereby extending the range of skills that the hierarchical system can learn and execute. This would further validate the system's scalability and adaptability.
2. Enhanced Observation Techniques: Incorporating advanced observation techniques like Vision Transformers could refine uncertainty inference, particularly for partial environmental changes. This would enhance the system's ability to handle more complex and occlusive tasks.
3. Robustness in Out-of-Distribution Scenarios: Developing methods to further enhance robustness in out-of-distribution scenarios would be valuable. Ensuring that the system can handle unexpected environmental changes and novel tasks will be crucial for widespread deployment.
4. Integration with Other Learning Frameworks: Exploring the integration of the proposed framework with other learning paradigms, such as reinforcement learning, could yield innovative hybrid models that combine the strengths of imitation learning and self-improvement over time.

In conclusion, the paper presents a thorough and detailed methodology for uncertainty-aware hierarchical skill inference in shared autonomy systems. Its contributions lay a solid groundwork for future enhancements and practical implementations in dynamic and complex robotic applications.