Learning about objects with human teachers

By Andrea Lockerd Thomaz, Maya Cakmak. 2009

93 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

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In Control and Systems Engineering

Citations in the corpus

Learning from Physical Human Corrections, One Feature at a Time (2018, background): “Indeed, sequential teaching may come more naturally to people collaborating with robots [21, 22], and reduces the burden on users to coordinate all aspects of the task simultaneously during each individual correction.”
Hierarchical Task Learning from Language Instructions with Unified Transformers and Self-Monitoring (2021, background): “Originated in the robotics community, learning from demonstration (LfD) (Thomaz and Cakmak, 2009; Argall et al., 2009) enables robots to learn a mapping from world states to robots’ manipulations based on human’s demonstration of desired robot behaviors.”
Children teach a care-receiving robot to promote their learning (2012, background): “The design of robot learners (Thomaz & Cakmak, 2009; Suay & Chernova, 2011) and the study of humans who interact with a teachable robot (Kim, Leyzberg, Tsui, & Scassellati, 2009) have been becoming major topics in HRI (Human-Robot Interaction) research ﬁeld.”
Language to Action: Towards Interactive Task Learning with Physical Agents (2018, background): “For example, learning from demonstration (LfD) [Thomaz and Cakmak, 2009; Argall et al. , 2009] learns a mapping from world states to robots’ manipulations based on the human demonstration of desired robot behaviors.”
Robots Show Us How to Teach Them: Feedback from Robots Shapes Tutoring Behavior during Action Learning (2014, background): “Another symbolic approach for learning object affordances by exploration involves a robot learner communicating that the object to be learned is too far or too close by tilting its neck to the upper limit and back [10].”
An Overview of Machine Teaching (2018, background): “This, in turn, can be useful in two ways: (i) insights gained here can be used to teach human users on how to optimally interact with and provide instructions to robots (e.g. for personalized robotic devices like Siri or Alexa) [1, 37,43], (ii) we could develop smarter learning algorithms for robots”
How Humans Teach Agents - A New Experimental Perspective (2012, background): “Other modes, though given less attention, have also been studied: learning from advice [21, 24], learning from numeric feedback or reward [15, 18, 37, 40], and allowing the human to shape the agent’s learning environment, facilitating the learning process [32, 38].”