Shared Autonomy via Hindsight Optimization for Teleoperation and Teaming (1706.00155v1)

Abstract: In shared autonomy, a user and autonomous system work together to achieve shared goals. To collaborate effectively, the autonomous system must know the user's goal. As such, most prior works follow a predict-then-act model, first predicting the user's goal with high confidence, then assisting given that goal. Unfortunately, confidently predicting the user's goal may not be possible until they have nearly achieved it, causing predict-then-act methods to provide little assistance. However, the system can often provide useful assistance even when confidence for any single goal is low (e.g. move towards multiple goals). In this work, we formalize this insight by modelling shared autonomy as a Partially Observable Markov Decision Process (POMDP), providing assistance that minimizes the expected cost-to-go with an unknown goal. As solving this POMDP optimally is intractable, we use hindsight optimization to approximate. We apply our framework to both shared-control teleoperation and human-robot teaming. Compared to predict-then-act methods, our method achieves goals faster, requires less user input, decreases user idling time, and results in fewer user-robot collisions.

• The paper introduces a novel POMDP framework with hindsight optimization that models a distribution of user goals for dynamic assistance.
• It demonstrates enhanced teleoperation performance, enabling faster task completion and reduced manual intervention in assistive robotics.
• In human-robot teaming, the approach minimizes idle time and collisions, thereby improving coordination in shared tasks.

Shared Autonomy via Hindsight Optimization for Teleoperation and Teaming

In the domain of robotics, shared autonomy is a nuanced collaboration where a user and an autonomous system jointly operate towards shared goals. Efficient collaboration necessitates that the autonomous system comprehend the user's target objectives. Traditionally, shared autonomy has employed a predict-then-act model to predict the user's intent before deciding on assistance actions. This paper presents an alternative approach, modeling shared autonomy as a Partially Observable Markov Decision Process (POMDP) optimized via hindsight optimization to allow the system to assist even under low-confidence predictions of user goals.

This framework is distinguished by its ability to provide assistance across a distribution of potential goals rather than committing to a single predicted objective. The paper's central contribution is the application of this framework to continuous state-action spaces using hindsight optimization, enabling real-time decision-making suitable for teleoperation and human-robot teaming scenarios.

Key Methodological Insights

The authors propose a set of novel definitions to handle user actions and the autonomous system's responses in shared autonomy. The core insight is the use of a POMDP model where the user's goal is treated as a hidden state. The robot's task is to maximize expected cost-to-goal across all potential goals, rather than focusing on a single high-confidence prediction. To make this computationally feasible, a QMDP or hindsight optimization approach is employed, which simplifies the decision process by assuming that future uncertainty resolves favorably.

Moreover, the authors delineate two main arenas of application:

1. Shared-Control Teleoperation: The framework is assessed in tasks like object-grasping and feeding, which are integral to assistive robotics. These tasks are complicated by the need for real-time joint control between human operators and robots, where predicting user intent can be challenging or ambiguous.
2. Human-Robot Teaming: In this setting, the goal is to coordinate actions such that conflicts (e.g., simultaneous access to a shared resource) are avoided, using the ability to dynamically adapt robot actions based on observed human intent.

Experimental Validation and Results

Detailed experimental evaluations show that the framework outperforms predict-then-act methodologies in several practical metrics. Specifically, in teleoperation:

• The novel approach enabled users to complete tasks more rapidly and with less manual intervention than traditional systems. This is a substantial advantage in user settings where physical input modality is limited (as in assistive devices).
• For human-robot teaming, the methodology reduced time spent idle and minimized collisions, indicating improved fluency and coordination between human and robot tasks.

Despite these advantages, the paper found that user preference varied, likely due to nuances in how autonomy is perceived versus control authority—a critical dimension in designing shared autonomy systems.

Implications and Future Directions

The paper extends our understanding of shared autonomy by alleviating the limitations of the predict-then-act model, showing potential implications across fields where the clarity of user intent is elusive. It underscores the possibility that systems can be more autonomously proactive in scenarios where traditional models would hesitate due to uncertainty.

Future research could extend this framework by incorporating more complex cost functions reflecting deeper aspects of user-robot interactions, such as safety and ergonomics. Additionally, exploring models that dynamically learn and adapt individual user preferences in real-time could enhance user satisfaction and effectiveness in shared tasks. Such advances may refine the dynamic balance between user input and autonomous decision-making, improving the adaptability and acceptance of such systems in wider applications.