Formalising learning from demonstration

• The paper describes and formalizes the concepts and assumptions involved in Learning from Demonstration (LFD), a common learning technique used in robotics. Inspired by the work on planning and actuation by LaValle, common LFD-related concepts like goal, generalization, and repetition are here defined, analyzed, and put into context. Robot behaviors are described in terms of trajectories through information spaces and learning is formulated as the mappings between some of these spaces. Finally, behavior primitives are introduced as one example of useful bias in the learning process, dividing the learning process into the three stages of behavior segmentation, behavior recognition, and behavior coordination.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Människa-datorinteraktion (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Human Computer Interaction (hsv//eng)

Nyckelord

Action selection

behavior

bias

generalization

goal

learning from demonstration

robot learning

segmentation

Cognitive science

Kognitionsvetenskap

computer and systems sciences

data- och systemvetenskap

Publikations- och innehållstyp

vet (ämneskategori)

rap (ämneskategori)