robotic assistants - intechopen

Robotic Assistants: Science meets Fiction

Carme Torras @

Robots>10

Doctors28

PhD students37

Technicians10

Support staff14

People:

Institut de Robòtica i Informàtica Industrial

Perception and Manipulation Lab

Robot manipulators in human environments

✦ Learning from demonstration ✦ Planning and perception for manipulation

✦ Perception of rigid and non-rigid objects

CELEBRATING 10 YEARS 33

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ELEBRATING Y E A R S

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1. From industrial to assistive robotics

2. Research challenges: illustrative projects

3. Ethical and social implications


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Presentation outline


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Assistance to disabled and elderly people

Urban guidance, shopping helpers, cleaning

Co-workers in workshops and factories

Training and education

Industrial robots Assistive robots

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World’s largest civilian robotics programme

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Industrial robots Assistive robots

Easy to program by non-experts

Intrinsically safe for people

Able to perceive and manipulate deformable objects

Tolerant to noisy perceptions and inaccurate actions

Capable of goal-directed execution

Collaborating with people

Programmed by experts

Caged

Rigid objects -

Accurate

Fixed sequences

Non-interactive

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Challenges

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Industrial robots







Programmed by experts

Caged

Rigid objects -

Accurate

Fixed sequences

Non-interactive

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Challenges Techniques







Initial guidance + reinforcement

Modelling robot dynamics

Visual learning + task-oriented descriptors

Probabilistic state and action representations / uncertainty

Learning to plan

Learning from demonstrations


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Learning to plan


Easy to program / safe for people

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Initial guidance + reinforcement learning

Dynamic Movement Primitives (DMP):

Compact, rescalable, intuitive parametrization

IROS'14

Easy to program / safe for people

Easy to program - bimanual skills

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Learning to plan


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Garment recognition and pose estimation

Usual approach:

Re-grasping to place garment in a standard configuration eases perception but is slow…

Our approach:

Involved computer vision and machine learning algorithms for informed (task-oriented) one-shot grasping

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Visual recognition for informed grasping

Clothing dataset with labelled parts

ICRA’12 - IROS’13 - EAAI, 2014

(Bag-of-Words + SVM) for part location

Visual recognition for informed grasping

Perceiving and manipulating other deformable objects

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Learning to plan


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IntellAct: Intelligent observation and execution of Actions and manipulations

Probabilistic representations / Learning to plan

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On-line learning Starts with no previous knowledge Initial demonstration --> rules Adapts to changes --> rule prob. updates

Active requests for teacher help Guide the teacher to minimize interaction Explain planning failures Sketch options: new actions, state changes

Learning to plan: RL + demonstrations

Two options (in red)

ICRA’14, Special Issue "AI and Robotics", 2015

Learning to plan: Teacher help

Learning to plan for goal-directed execution

IntellAct Online Perception

IntellAct Online Monitoring and Execution

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Learning to plan


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Learning to collaborate from demonstrations

AAAI'13


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Moral issues: military, legal liability, digital gap, …

Robotic assistants differ from other technologies in entering the domain of human feelings.

Ethical and social implications

How will human nature change with increasing H-R interaction?


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Artificial retinas, sensorized dresses, exoskeletons, telepresence… robotic prostheses expand our body

Robots and humans… two types of ties

Living with butlers and artificial nannies, learning from robotic teachers, sharing work and leisure with humanoids... will enhance our intellectual and social habits? will develop new ones?


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➢ N. Sharkey & A. Sharkey: "The crying shame of robot nannies: an ethical appraisal". (Pros and cons of robot nannies)

Classic science-fiction stories:

➢ I. Asimov (1950) "I, Robot" (protect vs. freedom) ➢ Ph.K. Dick (1955) "Nanny" (animate vs. inanimate) ➢ R. Bradbury (1969) "I sing the body electric" (acceptance-

immortality, sincerity)

Science meets fiction

2010: Special issue of the journal Interaction Studies


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Can the effects of technology on human evolution be predicted?

Methodological difficulties: ● Appearance of unforeseen uses for devices (Ihde, 2004) ● Limitations of language to describe the future: “it is

through technique that we perceive the sea as navigable” (Heidegger)

● Cannot be studied separately from the socio-cultural context: Social construction of reality (Berger and Luckmann, 1966)

Joint work with F. Ballesté (Humanities, UOC)


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“It is the relationships that we have constructed which in turn shape us”

Robert C. Solomon, “The Passions”

Robotic assistants: social and ethical implications

Neal Stephenson, “Innovation starvation”

“Science fiction provides coherent scenarios of a technology integrated into a society, into an economy, and into people’s lives”

The future holds exciting technoscientific, ethical and anthropological challenges

robotic assistants - intechopen

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