Visual learning by imitation with motor representations
By Manuel Lopes, José Santos-Victor. 2005
114 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Social Psychology, Human-Computer Interaction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Manuel Lopes
- Luis Montesano
- Ashutosh Saxena
- José Santos-Victor
- Hema Swetha Koppula
- Francisco S. Melo
- Alexandre Bernardino
- Rudhir Gupta
In Social Psychology
- Luis Montesano
- Manuel Lopes
- José Santos-Victor
- Francisco S. Melo
- Alexandre Bernardino
- Ashutosh Saxena
- Hema Swetha Koppula
- Ben Kenward
In Human-Computer Interaction
- Luis Montesano
- José Santos-Victor
- Manuel Lopes
- Francisco S. Melo
- Alexandre Bernardino
- Ashutosh Saxena
- Hema Swetha Koppula
- Rudhir Gupta
Citations in the corpus
- Learning human activities and object affordances from RGB-D videos (2012, background): “There is some recent work in interpreting human actions and interaction with objects (Lopes and Santos-Victor, 2005; Saxena et al., 2008; Aksoy et al., 2011; Konidaris et al., 2012; Li et al., 2012) in context of learning to perform actions from demonstrations.”
- Learning Object Affordances: From Sensory--Motor Coordination to Imitation (2008, methodology): “Affordances have also been used as prior distributions for action recognition in a Bayesian framework [9] or to perform selective attention in obstacle avoidance tasks [10].”
- A syntactic approach to robot imitation learning using probabilistic activity grammars (2013, unknown): “This biasing effect can be also interpreted as an affordance learning, similar to [43], where the recognition of an observed gesture depends on a context variable.”
- A Computational Model of Social-Learning Mechanisms (2009, result): “Finally, several works contrast with those referred above in that they are able to generate multiple social-learning behaviours [19,20,21].”
- A Developmental Roadmap for Learning by Imitation in Robots (2007, methodology): “Extra details of these processes are presented in [25].”
- A survey of robot learning from demonstration (2009, unknown): “A force-sensing glove is combined with vision-based motion tracking to teach grasping movements [43,44].”
- Abstraction Levels for Robotic Imitation: Overview and Computational Approaches (2010, methodology): “If we further take into account that the perceptual variables from the demonstrator must also be mapped from an allo- to an ego- frame of reference, the first of the above subproblems further subdivides into two other sub-problems: viewpoint transformation and sensory-motor matching [8, 22, 82, 83, ”