60 research outputs found
PhD Thesis Proposal: Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Resource optimization in health care, manufacturing, and military operations requires the careful choreography of people and equipment to effectively fulfill the responsibilities of the profession. However, resource optimization is a computationally challenging problem, and poorly utilizing resources can have drastic consequences. Within these professions, there are human domain experts who are able to learn from experience to develop strategies, heuristics, and rules-of-thumb to effectively utilize the resources at their disposal. Manually codifying these heuristics within a computational tool is a laborious process and leaves much to be desired. Even with a codified set of heuristics, it is not clear how to best insert an autonomous decision-support system into the human decision-making process. The aim of this thesis is to develop an autonomous computational method for learning domain-expert heuristics from demonstration that can support the human decision-making process. We propose a new framework, called apprenticeship scheduling, which learns and embeds these heuristics within a scalable resource optimization algorithm for real-time decision-support. Our initial investigation, comprised of developing scalable methods for scheduling and studying shared control in human-machine collaborative resource optimization, inspires the development of our apprenticeship scheduling approach. We present a promising, initial prototype for learning heuristics from demonstration and outline a plan for our continuing work
Heterogeneous Learning from Demonstration
The development of human-robot systems able to leverage the strengths of both
humans and their robotic counterparts has been greatly sought after because of
the foreseen, broad-ranging impact across industry and research. We believe the
true potential of these systems cannot be reached unless the robot is able to
act with a high level of autonomy, reducing the burden of manual tasking or
teleoperation. To achieve this level of autonomy, robots must be able to work
fluidly with its human partners, inferring their needs without explicit
commands. This inference requires the robot to be able to detect and classify
the heterogeneity of its partners. We propose a framework for learning from
heterogeneous demonstration based upon Bayesian inference and evaluate a suite
of approaches on a real-world dataset of gameplay from StarCraft II. This
evaluation provides evidence that our Bayesian approach can outperform
conventional methods by up to 12.8
Do People Trust Robots that Learn in the Home?
It is not scalable for assistive robotics to have all functionalities
pre-programmed prior to user introduction. Instead, it is more realistic for
agents to perform supplemental on site learning. This opportunity to learn user
and environment particularities is especially helpful for care robots that
assist with individualized caregiver activities in residential or nursing home
environments. Many assistive robots, ranging in complexity from Roomba to
Pepper, already conduct some of their learning in the home, observable to the
user. We lack an understanding of how witnessing this learning impacts the
user. Thus, we propose to assess end-user attitudes towards the concept of
embodied robots that conduct some learning in the home as compared to robots
that are delivered fully-capable. In this virtual, between-subjects study, we
recruit end users (care-givers and care-takers) from nursing homes, and
investigate user trust in three different domains: navigation, manipulation,
and preparation. Informed by the first study where we identify agent learning
as a key factor in determining trust, we propose a second study to explore how
to modulate that trust. This second, in-person study investigates the
effectiveness of apologies, explanations of robot failure, and transparency of
learning at improving trust in embodied learning robots.Comment: Presented at Machine Learning in Human-Robot Collaboration: Bridging
the Gap (ML HRC) workshop at HRI 202
FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings
Federated learning is a training paradigm that learns from multiple
distributed users without aggregating data on a centralized server. Such a
paradigm promises the ability to deploy machine-learning at-scale to a diverse
population of end-users without first collecting a large, labeled dataset for
all possible tasks. As federated learning typically averages learning updates
across a decentralized population, there is a growing need for personalization
of federated learning systems (i.e conversational agents must be able to
personalize to a specific user's preferences). In this work, we propose a new
direction for personalization research within federated learning, leveraging
both personal embeddings and shared context embeddings. We also present an
approach to predict these ``preference'' embeddings, enabling personalization
without backpropagation. Compared to state-of-the-art personalization
baselines, our approach achieves a 50\% improvement in test-time perplexity
using 0.001\% of the memory required by baseline approaches, and achieving
greater sample- and compute-efficiency.Comment: Andrew Silva and Pradyumna Tambwekar contributed equally towards this
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