Robot Interaction Prediction for Selective Human Assistance
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Solution Overview
Problem
Robots face challenges in selectively interacting with humans, as they struggle to identify and approach individuals who are willing and capable of assisting them, especially in crowded environments where people's preferences and availability vary greatly.
Innovation Solution
An incrementally learned interaction prediction model that uses contextual information from sensors like cameras and LIDAR to estimate the likelihood of successful interaction, allowing robots to generate scores for potential interactions and select the most appropriate individuals to communicate with, thereby optimizing energy use and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a robot attempts to interact with multiple people in a crowd, then the probability of finding a willing assistant increases, but energy consumption increases
Solution Approach 1:
The robot performs preliminary assessment of potential interactors using visual and sensor data before initiating communication. The system pre-identifies people who appear available and interested based on their behavior, position, and engagement cues, then selectively approaches only those pre-screened candidates rather than randomly approaching multiple people.
Solution Approach 2:
The robot uses its own sensor suite (cameras, microphones, LIDAR) to autonomously assess potential interactors and determine interaction likelihood. The system serves itself by internally processing sensory information to identify promising targets without requiring external guidance or trial-and-error interactions with unrelated individuals.
2Use of energy by moving object
If a robot uses simple interaction protocols, then energy consumption is reduced, but task completion capability is limited
Solution Approach 1:
The interaction protocol dynamically adapts its complexity based on the assessed availability and responsiveness of the human interactor. The robot begins with simple communication and only escalates to more complex task coordination protocols when the human demonstrates engagement and capability, thereby avoiding unnecessary energy expenditure on complex protocols with unresponsive targets.
Solution Approach 2:
The system changes operational parameters (communication frequency, interaction complexity, approach speed) based on real-time assessment of the human interactor's response. When a human shows interest or availability, the robot increases interaction intensity and task complexity; when unresponsive, it reduces engagement levels to conserve energy.
3Loss of time
If a robot approaches people quickly, then task completion time is reduced, but the likelihood of successful interaction decreases
Solution Approach 1:
The robot performs preliminary visual assessment and pre-screening of potential interactors from a distance using cameras and sensors before approaching. By identifying promising candidates in advance based on their behavior and engagement cues, the robot can move more quickly and directly toward confirmed targets rather than slowly scanning and approaching multiple uncertain candidates.
Solution Approach 2:
The robot continuously monitors feedback from potential interactors (visual attention, body language, verbal responses) during the approach phase and adjusts its speed and trajectory accordingly. When positive engagement signals are detected, the robot accelerates its approach; when negative signals appear, it slows down or redirects, optimizing both speed and success probability.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selective human-robot interaction. In some implementations, sensor data describing an environment of a robot is received, and a person in the environment of the robot is detected based on the sensor data. Scores indicative of properties of the detected person are generated based on the sensor data and processed using a machine learning model. Processing the scores can produce one or more outputs indicative of a likelihood that the detected person will perform a predetermined action in response to communication from the robot. Based on the one or more outputs of the machine learning model, the robot initiates communication with the detected person.