Robots Safely Leveraging Influence to Efficiently Accomplish Tasks in Dynamic Human-Robot Interactions

المفاهيم الأساسية

Robots can safely leverage their influence over human behavior to accomplish tasks more efficiently in dynamic human-robot interactions, while ensuring high safety rates.

الملخص

This paper presents a novel framework called SLIDE (Safely Leveraging Influence in Dynamic Environments) that enables robots to safely exploit their influence over human behavior to accomplish tasks more efficiently in dynamic human-robot interactions.

The key insights are:

1. Modeling Human Influence: The authors use a conditional behavior prediction (CBP) model to capture how the human's behavior is influenced by the robot's future actions. This allows the robot to reason about how it can shape the human's responses.

2. Belief-Space Reach-Avoid Games: The authors formulate the problem as a reach-avoid dynamic game played in the joint physical and robot belief space. This allows the robot to reason about how its uncertainty in the human's future behavior will evolve over time.

3. Offline Reinforcement Learning Solution: The authors leverage recent advances in reach-avoid reinforcement learning to solve this high-dimensional game offline, enabling the robot to learn policies that safely exploit influence.

Through extensive simulations in a 39-dimensional human-robot collaborative manipulation scenario, the authors show that SLIDE is less conservative than prior safe control approaches while staying safe even in the presence of out-of-distribution human behavior.

الإحصائيات

The robot and human arms are modeled as 2-link planar manipulators with joint torque control inputs bounded by box constraints.
The task involves reaching for one of four objects (two mugs and two bottles) on a tabletop, without colliding with each other.

اقتباسات

"Robots can influence people to accomplish their tasks more efficiently: autonomous cars can inch forward at an intersection to pass through, and tabletop manipulators can go for an object on the table first."
"However, a robot's ability to influence can also compromise the safety of nearby people if naively executed."

الرؤى الأساسية المستخلصة من

Robots that Learn to Safely Influence via Prediction-Informed Reach-Avoid Dynamic Games

by Ravi Pandya,... في arxiv.org 09-19-2024

https://arxiv.org/pdf/2409.12153.pdf

استفسارات أعمق

How can the SLIDE framework be extended to handle more complex human-robot interaction scenarios, such as multi-agent settings or tasks with more diverse objectives?

The SLIDE framework can be extended to accommodate more complex human-robot interaction scenarios by incorporating several enhancements. First, the framework could be adapted to a multi-agent setting by introducing additional layers of conditional behavior prediction (CBP) models that account for interactions among multiple humans and robots. This would involve developing a joint CBP model that predicts the actions of all agents based on their respective plans, thereby capturing the dynamics of influence in a multi-agent context.
Second, the reach-avoid dynamic game formulation could be expanded to include multiple objectives, allowing robots to prioritize tasks based on contextual factors such as urgency, safety, and efficiency. This could be achieved by integrating a multi-objective optimization approach within the SLIDE framework, where the robot evaluates trade-offs between competing objectives while still ensuring safety and influence.
Additionally, the belief-space formulation could be enhanced to incorporate a richer representation of human intentions and preferences, allowing the robot to adapt its strategies based on the evolving context of the interaction. This could involve using more sophisticated machine learning techniques, such as deep reinforcement learning, to continuously update the robot's understanding of human behavior and preferences in real-time.
Finally, incorporating feedback mechanisms where humans can express their preferences or intentions could further refine the SLIDE framework, enabling robots to adjust their influence strategies dynamically based on direct human input.

What are the potential ethical considerations and societal implications of robots safely leveraging influence over human behavior? How can these be addressed?

The potential ethical considerations and societal implications of robots safely leveraging influence over human behavior are multifaceted. One primary concern is the issue of autonomy and consent. When robots influence human decisions, there is a risk of undermining individual autonomy, leading to questions about whether humans are making free choices or being manipulated. To address this, it is crucial to establish clear guidelines and frameworks that ensure transparency in how robots influence human behavior. This could involve informing users about the robot's capabilities and the nature of its influence, allowing individuals to make informed decisions.
Another ethical consideration is the potential for bias in the influence exerted by robots. If the underlying models used for prediction and influence are trained on biased data, the robot may inadvertently reinforce harmful stereotypes or make unfair assumptions about human behavior. To mitigate this risk, it is essential to implement rigorous testing and validation processes for the predictive models, ensuring they are trained on diverse and representative datasets.
Moreover, the societal implications of such technologies must be carefully considered. The deployment of robots that influence human behavior could lead to shifts in social dynamics, particularly in environments like workplaces or homes. This necessitates ongoing dialogue among stakeholders, including ethicists, technologists, and the public, to navigate the complexities of human-robot interactions and establish norms that promote positive outcomes.
Finally, regulatory frameworks should be developed to govern the use of influence in robotics, ensuring that ethical standards are upheld and that the technology is used responsibly. This could involve creating oversight bodies that monitor the deployment of such systems and their impact on society.

Could the principles of SLIDE be applied to other domains beyond physical human-robot interaction, such as human-AI collaboration in decision-making or information sharing?

Yes, the principles of SLIDE can be effectively applied to other domains beyond physical human-robot interaction, particularly in human-AI collaboration in decision-making and information sharing. The core concepts of influence, safety, and predictive modeling are highly relevant in these contexts.
In decision-making scenarios, AI systems can leverage influence by providing recommendations or nudges that guide human users toward optimal choices. By employing a similar reach-avoid dynamic game framework, AI can model the potential outcomes of different decisions while considering the uncertainty in human preferences and behaviors. This would allow AI systems to suggest actions that maximize positive outcomes while minimizing risks, akin to how SLIDE ensures safety in human-robot interactions.
In the realm of information sharing, the SLIDE framework's conditional behavior prediction can be adapted to anticipate how users will respond to different types of information. For instance, an AI system could predict how a user might react to various data presentations or alerts, allowing it to tailor its communication strategies to enhance understanding and engagement. By modeling the influence of information on user behavior, AI can promote more effective information dissemination while ensuring that users are not overwhelmed or misled.
Furthermore, the belief-space formulation used in SLIDE can be applied to continuously update the AI's understanding of user preferences and behaviors, enabling more personalized and context-aware interactions. This adaptability is crucial in dynamic environments where user needs and contexts may change rapidly.
Overall, the principles of SLIDE provide a robust framework for enhancing human-AI collaboration, ensuring that influence is exerted safely and effectively across various domains.