Trust-Aware Assistance-Seeking Policy for Robots in Human-Robot Collaboration with Dual-Task Paradigm

核心概念

By modeling human trust and engagement dynamics, an optimal assistance-seeking policy for robots can be developed to improve overall team performance in collaborative tasks.

摘要

Bibliographic Information:

Mangalindan, D. H., & Srivastava, V. Assistance-Seeking in Human-Supervised Autonomy: Role of Trust and Secondary Task Engagement (Extended Version). arXiv preprint arXiv:2405.20118v3 (2024).

Research Objective:

This research investigates how a robot's assistance-seeking behavior affects human trust and performance in a dual-task scenario, aiming to design an optimal assistance-seeking policy that maximizes team performance.

Methodology:

The researchers conducted human-subject experiments using a dual-task paradigm where participants supervised a robot collecting objects while simultaneously performing a target-tracking task. They collected data on human trust ratings, task performance, and robot actions. Using this data, they developed and estimated models for human trust dynamics, target-tracking engagement dynamics, and human action selection probability. Finally, they designed an optimal assistance-seeking policy using Model Predictive Control (MPC) based on the estimated models.

Key Findings:

• Human trust in the robot is influenced by the robot's performance, actions, and the complexity of the task.
• Human engagement in the secondary task is affected by the robot's actions and their own trust in the robot.
• The optimal assistance-seeking policy for the robot is context-dependent, considering both human trust and engagement levels.
• The MPC-based policy, which considers human trust and engagement, outperforms a greedy baseline policy that only considers task complexity.

Main Conclusions:

By modeling human trust and engagement dynamics, an optimal assistance-seeking policy for robots can be developed to improve overall team performance in collaborative tasks. The policy should adapt to different task complexities and human states, seeking assistance when human trust is low or engagement in the secondary task is compromised.

Significance:

This research contributes to the field of human-robot collaboration by providing insights into the factors influencing human trust and engagement during collaborative tasks. The proposed MPC-based assistance-seeking policy offers a practical approach to improve the efficiency and effectiveness of human-robot teams.

Limitations and Future Research:

The study was limited to a specific dual-task scenario. Future research could explore the generalizability of the findings and the policy to other collaborative tasks and environments. Additionally, investigating the impact of different robot communication strategies on human trust and engagement could further enhance the design of assistance-seeking policies.

統計資料

When the robot operated autonomously, it had a success probability of 0.75 in high-complexity tasks and 0.96 in low-complexity tasks.
In high-complexity tasks, the robot asked for human assistance with a probability of 0.3, while in low-complexity tasks, the probability was 0.1.
For the target-tracking task, participants achieved a mean performance of 89% at slow speeds and 82% at normal speeds.
The MPC policy resulted in 16 interruptions from the human participants, while the greedy policy led to 23 interruptions.
The median cumulative reward scores were 65.75 for the MPC policy and 57 for the greedy policy.

引述

"Autonomous systems are often underutilized due to the lack of trust, defeating the purpose and benefits of using automation."
"In contrast, an excessive reliance or trust in automation can lead to misuse or abuse of the system."
"Supervisors often juggle multiple tasks, managing their own responsibilities while overseeing others. This dynamic also applies to human supervisors overseeing autonomous agents."
"The human should only intervene with the robot when absolutely necessary to prevent compromising their own fruit collection. Similarly, the robot should be designed to operate with minimal interference to the human’s tasks."

從以下內容提煉的關鍵洞見

Assistance-Seeking in Human-Supervised Autonomy: Role of Trust and Secondary Task Engagement (Extended Version)

by Dong Hae Man... 於 arxiv.org 10-29-2024

https://arxiv.org/pdf/2405.20118.pdf

深入探究

How can these findings be applied to design collaborative robots for more complex and dynamic real-world environments beyond the laboratory setting?

This study provides valuable insights into human-robot collaboration that can be applied to real-world environments. Here's how:

Context-Aware Assistance-Seeking: The study highlights the importance of context in robot assistance requests. In complex environments, robots should be designed to analyze various factors like task complexity, human workload (potentially assessed through physiological sensors or task performance metrics), and even environmental uncertainties before requesting help. This ensures that assistance is sought only when truly necessary, preventing unnecessary interruptions and fostering appropriate reliance on the robot.
Trust-Repair Mechanisms: Real-world applications inevitably involve robot failures. The study demonstrates that failures can significantly impact human trust. Therefore, robots should be equipped with trust-repair mechanisms. These could include providing clear explanations for failures, suggesting alternative solutions, or even exhibiting self-correcting behaviors.
Personalized Collaboration: The research emphasizes the dynamic nature of human trust and engagement. Real-world collaborative robots should be capable of adapting to individual users. This could involve learning from past interactions, recognizing patterns in trust and engagement levels, and personalizing assistance-seeking strategies accordingly. For instance, a robot could learn that a particular human supervisor prefers to handle certain sub-tasks autonomously and adjust its behavior to minimize interruptions in those areas.
Beyond Binary Actions: The current study focuses on binary robot actions (autonomous action or assistance request). In real-world scenarios, robots could benefit from a wider range of actions, such as requesting clarification from the human, suggesting partial solutions, or even deferring the decision to the human while providing relevant information. This allows for more nuanced and flexible collaboration.
Continuous Trust Calibration: The use of a particle filter for real-time estimation of trust and engagement is promising for real-world applications. By continuously monitoring and adapting to human behavior, robots can calibrate their actions to maintain appropriate trust levels and optimize team performance.
Moving beyond the laboratory setting will require addressing challenges like sensor noise, environmental uncertainties, and the broader range of tasks encountered in real-world scenarios. However, the principles of trust-aware assistance-seeking, continuous trust calibration, and personalized collaboration provide a strong foundation for designing effective collaborative robots for complex and dynamic environments.

Could providing the human with more control over the robot's actions, rather than just responding to assistance requests, lead to higher trust and better team performance?

Yes, providing humans with more control and agency in human-robot collaboration can significantly impact trust and team performance. Here's why:

Enhanced Feeling of Safety and Predictability: Allowing humans to adjust the robot's actions, set boundaries, or even override decisions can increase their sense of control over the situation. This is particularly crucial in high-risk or safety-critical domains, where trust is paramount. Knowing they have the final say can mitigate anxiety and encourage humans to rely on the robot more willingly.
Facilitating Learning and Calibration: Granting humans more control allows them to "train" the robot according to their preferences and working styles. By observing human interventions and adjustments, the robot can learn more effectively about human expectations and adapt its future actions accordingly. This iterative process of feedback and adaptation can lead to a more calibrated and trusting collaboration.
Promoting Shared Mental Models: When humans have more input into the robot's decision-making process, it fosters a sense of shared understanding and goals. This shared mental model is crucial for effective teamwork, as it reduces misunderstandings and allows for smoother coordination.
Moving Beyond Task Allocation: Current approaches often focus on task allocation – deciding who does what. Providing humans with more control shifts the paradigm towards shared control, where humans and robots collaborate on decisions and actions. This can lead to more flexible and adaptive teamwork, particularly in dynamic environments where pre-defined task divisions might not be optimal.
However, simply providing more control is not a guaranteed solution. The interface through which control is exerted must be intuitive, user-friendly, and not overly complex. Excessive control options can overwhelm the human and be counterproductive. The key is to find the right balance between automation and human agency, allowing humans to guide and influence the robot's actions without being burdened with micromanagement.

If trust is a form of shared vulnerability, how can we design robots that are capable of exhibiting vulnerability, and would that lead to more effective collaboration?

The concept of trust as shared vulnerability is intriguing in the context of human-robot interaction. While robots don't experience vulnerability in the same way humans do, we can design them to exhibit behaviors that humans might interpret as vulnerability, potentially fostering trust. Here are some possibilities:

Transparency and Uncertainty Communication: Robots can be designed to express uncertainty in their decisions or predictions. Instead of presenting a confident but potentially inaccurate answer, a robot could communicate its confidence level, acknowledge potential errors, or even ask for clarification from the human. This transparency, while seemingly exposing limitations, can actually build trust by being more honest about capabilities.
Seeking Help Appropriately: As explored in the study, robots requesting assistance in a well-calibrated manner can improve trust. This act of seeking help can be seen as an expression of vulnerability, acknowledging limitations and relying on the human partner.
Learning from Mistakes: When a robot makes a mistake, instead of simply moving on, it could be designed to acknowledge the error, attempt to understand the cause, and explicitly demonstrate that it has learned from the experience. This process of reflection and improvement, mirroring human learning, can signal a capacity for growth and vulnerability.
Showing Effort and Perseverance: Robots could be programmed to visibly demonstrate effort when tackling challenging tasks. This could involve showing different approaches being considered, expressing "frustration" when encountering difficulties, or even seeking encouragement from the human partner. These behaviors, while anthropomorphic, can make the robot appear more relatable and evoke empathy, potentially increasing trust.
However, designing robots to exhibit vulnerability requires careful consideration:

Avoiding Over-Anthropomorphism: While some degree of anthropomorphism can be beneficial, excessive or inappropriate displays of vulnerability can backfire, leading to perceptions of incompetence or manipulation.
Context is Key: The type and degree of vulnerability exhibited should be context-dependent. A robot working in a high-stakes surgical setting might express uncertainty differently than a robot assisting with household chores.
Cultural Considerations: Perceptions of vulnerability and its impact on trust can vary significantly across cultures. Robots should be designed with cultural sensitivity in mind.
Designing robots that can appropriately exhibit vulnerability is a complex challenge. However, by carefully considering the context, avoiding over-anthropomorphism, and focusing on genuine expressions of limitations and learning, we can potentially design robots that are not only more trustworthy but also more effective collaborators.