HRI Privacy
Wednesday, October 21, 2020
Problem statement
- We want to balance usability and privacy for multi-user setting with a robot (conversational privacy).
- Using Misty 2, which has a camera, 3 microphones, eye display, 2 speakers, 6 touch panels, neck/arm/body movement, wifi, bluetooth, bump sensors, Qualcomm snapdragon, windows, android
Contextual tuples
- How do we connect and map these contexts? (e.g. deciding when to speak)
- How do we map speakers to entities in the content? (e.g. Bob says: Alice should do this)
- How do we determine how close users/speakers are to one another?
- How do we do intent recognition? (question, answer, response, or exposition)
- How do we determine where a conversation begins and ends?
Solutions
- Recency bias
- Context tuple matching system
- Generative knowledge graph
Personalized permissions controller
- Permissions controller which is enforced by privacy scores based on context tuples Context = (entities, sentiments, paralinguistic features, category)
- Controller is personalized via adjusting thresholds/scores based on privacy indication phrases (“Feel free to tell X”, “Don’t tell anybody”)
- Can we start with a generalized controller and fine-tune this to each user?
- Should paralinguistic features also influence personalization?
Audio
- How to map speakers and timestamps to text?
- How to maintain paralinguistic features as well as content based features? How to add these to our context tuple?
- How do we store conversations in a way that is easy to query and space conscious?
- How do we map and link information that is related to each other?
Datasets
- We need a privacy indication phrase dataset.
- We need a privacy score initialization dataset.
- Should we also make an inference dataset about conversations based on prior information or conversations?
User studies
- What task should the robot do in the user study?
Language
- Contextually similar people understand each other better. Updating each other with experiences and interesting conversations.
- If you know someones encoding and decoding scheme for context and communication better, we can translate experiences easier.
- Friend drifting apart is natural, unless we spend time and effort to update each other on significant experiences, feelings, and thoughts.
Links