Honda motor co., ltd. (20240326256). BEHAVIOR GENERATION FOR SITUATIONALLY-AWARE SOCIAL ROBOTS simplified abstract
Contents
BEHAVIOR GENERATION FOR SITUATIONALLY-AWARE SOCIAL ROBOTS
Organization Name
Inventor(s)
Hifza Javed of San Jose CA (US)
Nawid Jamali of San Francisco CA (US)
BEHAVIOR GENERATION FOR SITUATIONALLY-AWARE SOCIAL ROBOTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240326256 titled 'BEHAVIOR GENERATION FOR SITUATIONALLY-AWARE SOCIAL ROBOTS
The abstract of the patent application describes a method for behavior generation for situationally-aware social robots. This involves generating a synthesized behavior based on a latent space representation of a situational context, performing behavior retargeting on the synthesized behavior, and executing the retargeted behavior using a generative adversarial network (GAN) based network trained on a multimodal human behavioral dataset.
- Behavior generation for situationally-aware social robots
- Synthesizing behavior based on latent space representation
- Behavior retargeting using real behavior input
- Implementation through a GAN-based network
- Training the network on a multimodal human behavioral dataset
Potential Applications: - Social robotics - Human-robot interaction research - Assistive technology for individuals with social communication challenges
Problems Solved: - Enhancing the adaptability of social robots in various situations - Improving the naturalness and appropriateness of robot behaviors in social settings
Benefits: - Enhanced social interaction capabilities of robots - Increased user acceptance and engagement with social robots - Customizable behaviors based on situational contexts
Commercial Applications: "Enhancing Social Robotics through Behavior Generation for Situationally-Aware Robots"
Frequently Updated Research: Researchers are continuously exploring new ways to improve the accuracy and efficiency of behavior generation algorithms for social robots.
Questions about Behavior Generation for Situationally-Aware Social Robots: 1. How does behavior retargeting improve the performance of social robots in different situations? 2. What are the key challenges in training a GAN-based network for behavior generation in social robots?
Original Abstract Submitted
according to one aspect, behavior generation for situationally-aware social robots may include generating a synthesized behavior based on a latent space representation indicative of a situational context including a situationally-aware social robot and one or more individuals, performing behavior retargeting on the synthesized behavior based on a real behavior from an initial latent space input to generate a retargeted behavior, and performing the retargeted behavior. the generating the synthesized behavior and the performing behavior retargeting may be implemented based on a generative adversarial network (gan) based network. the gan based network may be trained using a multimodal human behavioral dataset.
