insight - Human-robot interaction safety - # Cognitive-grounded safe human-aware motion generation

Expectable Motion Unit: Avoiding Hazardous Involuntary Human Motions in Human-Robot Interaction

Q: How can the EMU be extended to adapt the velocity limit dynamically based on the user's cognitive state and level of awareness?

To extend the EMU to dynamically adjust the velocity limit based on the user's cognitive state and level of awareness, additional sensors and data inputs can be incorporated into the system. These sensors can monitor the user's physiological responses, such as heart rate variability, skin conductance, and facial expressions, to gauge their cognitive state and emotional reactions. By integrating these inputs into the EMU framework, the system can continuously assess the user's mental state and adjust the robot's velocity limit in real-time. Furthermore, machine learning algorithms can be employed to analyze the data from these sensors and predict the user's cognitive state based on patterns and trends. By training the system on a dataset of user responses and corresponding cognitive states, the EMU can learn to recognize indicators of heightened arousal, stress, or distraction that may lead to involuntary motions. This adaptive approach allows the EMU to proactively modulate the robot's speed to maintain a safe and comfortable interaction environment based on the user's cognitive fluctuations.

Q: How can the EMU concept be generalized to different robot types, interaction scenarios, and motion parameters beyond velocity, such as acceleration and jerk?

To generalize the EMU concept to different robot types, interaction scenarios, and motion parameters beyond velocity, such as acceleration and jerk, the framework can be expanded to consider a broader range of human-robot interaction dynamics. This expansion involves incorporating additional safety metrics and constraints related to acceleration, jerk, and other motion parameters that can impact the likelihood of involuntary human motions. One approach is to develop a comprehensive risk assessment model that accounts for various motion parameters and their effects on human safety and comfort. By conducting experiments and collecting data on how different motion profiles influence human responses, the EMU can be enhanced to include constraints on acceleration, jerk, and other dynamic factors. This enriched framework would enable the EMU to generate motion trajectories that not only consider velocity but also optimize acceleration profiles and jerk magnitudes to minimize the risk of triggering involuntary human motions. Moreover, the EMU can be adapted to different robot types and interaction scenarios by tailoring the safety thresholds and expectation curves to specific contexts. For instance, in collaborative industrial settings versus social HRI environments, the EMU parameters may vary to accommodate distinct user expectations and safety requirements. By customizing the EMU framework to suit diverse applications and robot capabilities, it can effectively address a wide range of human-robot interaction scenarios while prioritizing safety and user comfort.

Q: What other human factors, besides distance and velocity, could influence the occurrence of involuntary human motions in HRI, and how can they be incorporated into the EMU framework?

In addition to distance and velocity, several other human factors can influence the occurrence of involuntary human motions in HRI. These factors include: Emotional State: Emotions such as fear, anxiety, or surprise can trigger involuntary reactions. Monitoring facial expressions, heart rate variability, and skin conductance can provide insights into the user's emotional state. Cognitive Load: High cognitive load or distraction can lead to delayed reactions or startle responses. Eye-tracking technology and cognitive workload assessments can help gauge the user's mental burden. Experience and Familiarity: Users' prior experience with robots and their comfort level with technology can impact their reactions. Surveys and user feedback can capture this information. Physical Abilities: Users' physical capabilities and limitations, such as mobility impairments or disabilities, can affect their ability to react quickly to robot movements. Personalized profiles can account for these factors. To incorporate these human factors into the EMU framework, a multi-modal approach combining physiological sensors, behavioral analysis, and user feedback is essential. By integrating data from these sources and leveraging machine learning algorithms, the EMU can adapt its safety parameters based on a holistic understanding of the user's emotional, cognitive, and physical state. This comprehensive approach ensures that the EMU accounts for a wide range of human factors to enhance safety and user experience in HRI scenarios.

Core Concepts

The Expectable Motion Unit (EMU) ensures that the probability of hazardous involuntary human motions (IM) is not exceeded during human-robot interaction by limiting the robot's velocity based on an experimental model of IM occurrence.

Abstract

The authors propose the Expectable Motion Unit (EMU) to avoid potentially dangerous human involuntary motions (IM) in human-robot interaction (HRI). Through an exploratory study with 29 participants, they investigate the influence of robot motion parameters (velocity and distance) on the probability of IM occurrence in a common HRI scenario.
The key findings are:

The relative frequency of IM decreases as the robot-human distance increases and the robot velocity decreases.
An experimental risk matrix is derived, mapping robot velocity and distance to the probability of IM occurrence.
Expectation curves are defined based on the risk matrix, representing the maximum robot velocity that keeps the IM probability below a specified threshold.
The EMU is implemented by integrating the expectation curves into the robot motion generation, limiting the velocity to avoid exceeding the IM probability threshold.
The EMU is combined with the established Safe Motion Unit (SMU) to ensure both physical and psychological safety.
A validation experiment with 20 participants shows that the EMU successfully avoids IM in 5 out of 6 cases, significantly reducing IM compared to the previous experiment without EMU.
The proposed framework integrates cognitive-grounded safety aspects with well-established physical safety considerations, improving the acceptance and trustworthiness of human-robot collaboration.

Stats

The robot velocity was varied from 0.25 m/s to 1 m/s.
The robot-human distance was varied from 0 m to 0.25 m in 0.05 m steps.

Quotes

"Even if the robot behaviour is regarded as biomechanically safe, humans may still react with a rapid involuntary motion (IM) caused by a startle or surprise."
"The EMU aims to ensure that human expectation is fulfilled at any time. This avoids startle and surprise reactions, which can cause possibly hazardous human IM."
"In our preliminary validation, we observe that the desired 15% threshold was satisfied for robot-human distances > 5 cm. However, for dh ≥0 cm we observe 24 −36% IMO."

Key Insights Distilled From

Expectable Motion Unit

by Robin Jeanne... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2109.07201.pdf

Deeper Inquiries

How can the EMU be extended to adapt the velocity limit dynamically based on the user's cognitive state and level of awareness?

To extend the EMU to dynamically adjust the velocity limit based on the user's cognitive state and level of awareness, additional sensors and data inputs can be incorporated into the system. These sensors can monitor the user's physiological responses, such as heart rate variability, skin conductance, and facial expressions, to gauge their cognitive state and emotional reactions. By integrating these inputs into the EMU framework, the system can continuously assess the user's mental state and adjust the robot's velocity limit in real-time.
Furthermore, machine learning algorithms can be employed to analyze the data from these sensors and predict the user's cognitive state based on patterns and trends. By training the system on a dataset of user responses and corresponding cognitive states, the EMU can learn to recognize indicators of heightened arousal, stress, or distraction that may lead to involuntary motions. This adaptive approach allows the EMU to proactively modulate the robot's speed to maintain a safe and comfortable interaction environment based on the user's cognitive fluctuations.

How can the EMU concept be generalized to different robot types, interaction scenarios, and motion parameters beyond velocity, such as acceleration and jerk?

To generalize the EMU concept to different robot types, interaction scenarios, and motion parameters beyond velocity, such as acceleration and jerk, the framework can be expanded to consider a broader range of human-robot interaction dynamics. This expansion involves incorporating additional safety metrics and constraints related to acceleration, jerk, and other motion parameters that can impact the likelihood of involuntary human motions.
One approach is to develop a comprehensive risk assessment model that accounts for various motion parameters and their effects on human safety and comfort. By conducting experiments and collecting data on how different motion profiles influence human responses, the EMU can be enhanced to include constraints on acceleration, jerk, and other dynamic factors. This enriched framework would enable the EMU to generate motion trajectories that not only consider velocity but also optimize acceleration profiles and jerk magnitudes to minimize the risk of triggering involuntary human motions.
Moreover, the EMU can be adapted to different robot types and interaction scenarios by tailoring the safety thresholds and expectation curves to specific contexts. For instance, in collaborative industrial settings versus social HRI environments, the EMU parameters may vary to accommodate distinct user expectations and safety requirements. By customizing the EMU framework to suit diverse applications and robot capabilities, it can effectively address a wide range of human-robot interaction scenarios while prioritizing safety and user comfort.

What other human factors, besides distance and velocity, could influence the occurrence of involuntary human motions in HRI, and how can they be incorporated into the EMU framework?

In addition to distance and velocity, several other human factors can influence the occurrence of involuntary human motions in HRI. These factors include:

Emotional State: Emotions such as fear, anxiety, or surprise can trigger involuntary reactions. Monitoring facial expressions, heart rate variability, and skin conductance can provide insights into the user's emotional state.

Cognitive Load: High cognitive load or distraction can lead to delayed reactions or startle responses. Eye-tracking technology and cognitive workload assessments can help gauge the user's mental burden.

Experience and Familiarity: Users' prior experience with robots and their comfort level with technology can impact their reactions. Surveys and user feedback can capture this information.

Physical Abilities: Users' physical capabilities and limitations, such as mobility impairments or disabilities, can affect their ability to react quickly to robot movements. Personalized profiles can account for these factors.

To incorporate these human factors into the EMU framework, a multi-modal approach combining physiological sensors, behavioral analysis, and user feedback is essential. By integrating data from these sources and leveraging machine learning algorithms, the EMU can adapt its safety parameters based on a holistic understanding of the user's emotional, cognitive, and physical state. This comprehensive approach ensures that the EMU accounts for a wide range of human factors to enhance safety and user experience in HRI scenarios.

Expectable Motion Unit: Avoiding Hazardous Involuntary Human Motions in Human-Robot Interaction