Managing learning interactions for collaborative robot learning

dc.contributor.advisor Chernova, Sonia
dc.contributor.author Bullard, Kalesha
dc.contributor.committeeMember Isbell, Charles
dc.contributor.committeeMember Christensen, Henrik I.
dc.contributor.committeeMember Mataric, Maja
dc.contributor.committeeMember Thomaz, Andrea L.
dc.contributor.department Interactive Computing
dc.date.accessioned 2020-01-14T14:46:10Z
dc.date.available 2020-01-14T14:46:10Z
dc.date.created 2019-12
dc.date.issued 2019-09-11
dc.date.submitted December 2019
dc.date.updated 2020-01-14T14:46:10Z
dc.description.abstract Robotic assistants should be able to actively engage their human partner(s) to generalize knowledge about relevant tasks within their shared environment. Yet a key challenge is not all human partners will be proficient at teaching; furthermore, humans should not be held accountable for tracking a robot’s knowledge over time in a dynamically changing environment, across multiple tasks. Thus, it is important to enable these interactive robots to characterize their own uncertainty and equip them with an information gathering policy for asking the appropriate questions of their human partners to resolve that uncertainty. In this way, the robot shares the responsibility in guiding its own learning process and is a collaborator in the learning. Additionally, given the robot requires some tutelage from its partner, awareness of constraints on the teacher’s time and cognitive resources available for devoting to the interaction could help the agent to use the time allotted more wisely. This thesis examines the problem of enabling a robotic agent to leverage structured interaction with a human partner for acquiring concepts relevant to a task it must later perform. To equip the agent with the desired concept knowledge, we first explore the paradigm of Learning from Demonstration for the acquisition of (1) training instances as examples of task-relevant concepts and (2) informative features for appropriately representing and discriminating between task-relevant concepts. Given empirical evidence that a human partner can be helpful to the agent in solving the concept learning problem, we subsequently investigate the design of algorithms that enable the robot learner to autonomously manage interaction with its human partner, using a questioning policy to actively gather both instance and feature information. This thesis seeks to investigate the following hypothesis: In the context of robot learning from human demonstrations in changeable and resource-constrained environments, enabling the robot to actively elicit multiple types of information through questions, and to reason about what question to ask and when, leads to improved learning performance.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/62294
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Interactive robot learning
dc.subject Active learning
dc.title Managing learning interactions for collaborative robot learning
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Chernova, Sonia
local.contributor.corporatename College of Computing
local.contributor.corporatename School of Interactive Computing
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relation.isOrgUnitOfPublication aac3f010-e629-4d08-8276-81143eeaf5cc
thesis.degree.level Doctoral
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