Assistive robotics: helping with tasks for fun and profit. Neil Bell 3 March 2016 CMSC691-HRI
Who has... Five to ten year goals? 1. Finish school? 2. Pay off the car loan? 3. First (or second) child?
Who has... Five to ten year goals? 1. Finish school? 2. Pay off the car loan? 3. First (or second) child? Barriers to these goals? 1. Injury? 2. Family emergency? 3. Relative growing old, requiring assistance?
Who has... Five to ten year goals? 1. Finish school? 2. Pay off the car loan? 3. First (or second) child? Barriers to these goals? 1. Injury? 2. Family emergency? 3. Relative growing old, requiring assistance? Today’s focus: leveraging robotic assistance to ease the burden of extra or increasingly more difficult tasks.
Papers du jour 1. The Domesticated Robot: Design Guidelines for Assisting Older Adults to Age in Place. J. Beer, C. Smarr, T. Chen, A. Prakash, T. Mitzner, C. Kemp, W. Rogers. HRI - Living and Working with Service Robots, 2012. 2. Online Development of Assistive Robot Behaviors for Collaborative Manipulation and Human-Robot Teamwork. B. Hayes, B. Scassellati. AAAI 2014.
Papers du jour 1. The Domesticated Robot: Design Guidelines for Assisting Older Adults to Age in Place. J. Beer, C. Smarr, T. Chen, A. Prakash, T. Mitzner, C. Kemp, W. Rogers. HRI - Living and Working with Service Robots, 2012. 2. Online Development of Assistive Robot Behaviors for Collaborative Manipulation and Human-Robot Teamwork. B. Hayes, B. Scassellati. AAAI 2014.
Aging in place Goal: 1. Retain or enhance functionality despite age-related changes a. Cognition - less working memory b. Physical - arthritis or pain c. Perception - senses weakened, vision loss 2. Therefore, eliminate need for relocation to satisfy or substitute for goal #1.
Adapting to barriers, measuring successful aging Selection - Development and commitment to personal goals Reframe or update goals based on life events and changes
Adapting to barriers, measuring successful aging Selection - Development and commitment to personal goals Reframe or update goals based on life events and changes Optimization - Increasing odds of success Investment of time and energy to behaviors that support chosen goals
Adapting to barriers, measuring successful aging Selection - Development and commitment to personal goals Reframe or update goals based on life events and changes Optimization - Increasing odds of success Investment of time and energy to behaviors that support chosen goals Compensation - Regulation of loss Use mechanisms to prevent or balance age-related changes
Compensatory mechanisms Psychological, such as mnemonics, memory aids.
Compensatory mechanisms Psychological, such as mnemonics, memory aids. Technological 1. Hearing aids 2. Wheelchairs 3. Eyeglasses
Compensatory mechanisms Psychological, such as mnemonics, memory aids. Technological 1. Hearing aids 2. Wheelchairs 3. Eyeglasses 4. Robots
Compensatory mechanisms Psychological, such as mnemonics, memory aids. Technological 1. Hearing aids 2. Wheelchairs 3. Eyeglasses 4. Robots “Competence” is dynamic, capturing how a person functions in isolation. Relocation decisions depend heavily on individual’s level of competence.
Purpose of the paper Identifies daily upkeep tasks as having highest potential for assistive robotics.
Purpose of the paper Identifies daily upkeep tasks as having highest potential for assistive robotics. 1. Assess older adults’ preference for assistance from robots or humans on upkeep tasks. 2. Understand older adults’ opinions of using a home robot. 3. Consider implications of findings for directing improvement efforts for designing home assistive robots.
Method 1. Questionnaire a. Technology experience b. Demographics, health, and current living situation
Method 1. Questionnaire a. Technology experience b. Demographics, health, and current living situation 2. Conduct group interviews with adults ranging in age from 65 to 93.
Method 1. Questionnaire a. Technology experience b. Demographics, health, and current living situation 2. Conduct group interviews with adults ranging in age from 65 to 93. 3. Assess familiarity with robots a. Most were familiar with concept of robots. b. Few had controlled or interacted with one.
Method 1. Questionnaire a. Technology experience b. Demographics, health, and current living situation 2. Conduct group interviews with adults ranging in age from 65 to 93. 3. Assess familiarity with robots a. Most were familiar with concept of robots. b. Few had controlled or interacted with one. 4. Introduce participants to capabilities of Personal Robot 2 (PR2).
Method 1. Questionnaire a. Technology experience b. Demographics, health, and current living situation 2. Conduct group interviews with adults ranging in age from 65 to 93. 3. Assess familiarity with robots a. Most were familiar with concept of robots. b. Few had controlled or interacted with one. 4. Introduce participants to capabilities of Personal Robot 2 (PR2). 5. Assistance Preference Checklist
Personal Robot 2
Assistance Preference Checklist Goal: assess how participants’ preferences (robot vs. human) vary per task Process: 1. Assume robot could perform task to the level of a human 2. Imagine participant needed assistance on the given task 3. Rate preference on scale a. Human-only (1) b. No preference (3) c. Robot only (5)
Results - cleaning Participants preferred robots over humans in 28 of 48 tasks, including many cleaning tasks. (M > 3.00 => preference for robot assistance, where 3.00 = no preference)
Results - cleaning Other tasks provided less decisive results. (M ≅ 3.00, where 3.00 = no preference)
Results - fetching Fetching tasks also more geared towards robotic assistance. (M > 3.00 => preference for robot assistance, where 3.00 = no preference)
Interview results - robot pros and cons Coding scheme used to identify patterns and themes from the discussion 1. Transcript segmented by the researcher 2. Segments categorized into groups: pro and con 3. Patterns emerge, identifying commonalities in the participant responses
Pro & con examples Pro: Con: Compensation Damage to environment Time saving Dependency Delegation of undesirable task Mental model Effort saving Reliability in the system Optimization Storage & space requirements
Final design recommendations Customizability Tailor behavior to user preference Interaction Cooperative robot-human effort Manipulation Level of dexterous manipulation Payload Range of weight expected to work with Range of motion Size of kinematic workspace (high/low/near/far) Storage & size Physical attributes such as footprint, height, mass.
Papers du jour 1. The Domesticated Robot: Design Guidelines for Assisting Older Adults to Age in Place. J. Beer, C. Smarr, T. Chen, A. Prakash, T. Mitzner, C. Kemp, W. Rogers. HRI - Living and Working with Service Robots, 2012. 2. Online Development of Assistive Robot Behaviors for Collaborative Manipulation and Human-Robot Teamwork. B. Hayes, B. Scassellati. AAAI 2014.
Transition If the previous paper can be seen as the “why”, this is the “how”. General agreement: 1. Human-robot teaming can improve efficiency, quality of life, and safety. 2. Robots: a. Provide assistance when useful. b. Do dull or undesirable tasks when possible. c. Assist with dangerous tasks when feasible Key contribution: One possible process and training model for enabling a robot to learn from demonstration.
Vocabulary Learning by demonstration Providing input to a learning system without complex interfaces. Novice operators can demo, and even verbally describe the process. Algorithms can be robust to inconsistencies of inexperienced trainers. Key point: Learn the human’s intent rather than solely a sequence of actions.
Learning by demonstration Consider the concerns that participants had from the previous paper: 1. “I keep thinking of it in terms of how it could help prepare my food but I don’t know whether robots could cook.” 2. “I can see that if it does laundry, it needs to be able to sort by color. I can see that that would be a con and it couldn’t do it.” 3. “You tell him to bring glasses, he brings you a pair of shoes.” Learning by demonstration is designed to be flexible and efficient, quick to converge, especially as new tasks are added.
Vocabulary Markov Decision Process (MDP) A generalized structure for efficient representation of flexible, arbitrarily complex options, capturing closed-loop policies and action sequences.
Vocabulary Markov Decision Process (MDP) A generalized structure for efficient representation of flexible, arbitrarily complex options, capturing closed-loop policies and action sequences. S : a set of possible states ( s in S ) that the agent can be in at any moment. A : a set of possible actions ( a in A ) that the agent can take. R ( s ′ | s , a ) : reward for arriving at state s ′ having taken action a from state s . P ( s ′ | s , a ) : probability that taking action a from state s actually results in arriving at state s ′ . Quite necessary in stochastic settings (“robotics...duh”).
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