[HTML][HTML] Interactive task learning via embodied corrective feedback
M Appelgren, A Lascarides - Autonomous Agents and Multi-Agent …, 2020 - Springer
This paper addresses a task in Interactive Task Learning (Laird et al. IEEE Intell Syst 32: 6–
21, 2017). The agent must learn to build towers which are constrained by rules, and …
21, 2017). The agent must learn to build towers which are constrained by rules, and …
[HTML][HTML] Knowledge enhanced bottom-up affordance grounding for robotic interaction
W Qu, X Li, X Jin - PeerJ Computer Science, 2024 - peerj.com
With the rapid advancement of robotics technology, an increasing number of researchers
are exploring the use of natural language as a communication channel between humans …
are exploring the use of natural language as a communication channel between humans …
Not cheating on the Turing Test: towards grounded language learning in Artificial Intelligence
L Alberts - arXiv preprint arXiv:2206.14672, 2022 - arxiv.org
Recent hype surrounding the increasing sophistication of language processing models has
renewed optimism regarding machines achieving a human-like command of natural …
renewed optimism regarding machines achieving a human-like command of natural …
Interactive task learning from corrective feedback
M Appelgren - 2022 - era.ed.ac.uk
In complex teaching scenarios it can be difficult for teachers to exhaustively express all
information a learner requires to master a task. However, the teacher, who will have …
information a learner requires to master a task. However, the teacher, who will have …
Learning structured task related abstractions
SV Penkov - 2019 - era.ed.ac.uk
As robots and autonomous agents are to assist people with more tasks in various domains
they need the ability to quickly gain contextual awareness in unseen environments and …
they need the ability to quickly gain contextual awareness in unseen environments and …
Spatializing Symbolic Structures for the Gap
T Dong, T Dong - A Geometric Approach to the Unification of Symbolic …, 2021 - Springer
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Learning to make decisions with unforeseen possibilities
C Innes - 2019 - era.ed.ac.uk
Methods for learning optimal policies often assume that the way the domain is
conceptualised—the possible states and relevant actions that are needed to solve one's …
conceptualised—the possible states and relevant actions that are needed to solve one's …
Reasoning about Unforeseen Possibilities During Policy Learning
Methods for learning optimal policies in autonomous agents often assume that the way the
domain is conceptualised---its possible states and actions and their causal structure---is …
domain is conceptualised---its possible states and actions and their causal structure---is …