2,151 research outputs found
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
Assistive robotics: research challenges and ethics education initiatives
Assistive robotics is a fast growing field aimed at helping healthcarers in hospitals, rehabilitation centers and nursery homes, as well as empowering people with reduced mobility at home, so that they can autonomously fulfill their daily living activities. The need to function in dynamic human-centered environments poses new research challenges: robotic assistants need to have friendly interfaces, be highly adaptable and customizable, very compliant and intrinsically safe to people, as well as able to handle deformable materials.
Besides technical challenges, assistive robotics raises also ethical defies, which have led to the emergence of a new discipline: Roboethics. Several institutions are developing regulations and standards, and many ethics education initiatives include contents on human-robot interaction and human dignity in assistive situations.
In this paper, the state of the art in assistive robotics is briefly reviewed, and educational materials from a university course on Ethics in Social Robotics and AI focusing on the assistive context are presented.Peer ReviewedPostprint (author's final draft
From explanation to synthesis: Compositional program induction for learning from demonstration
Hybrid systems are a compact and natural mechanism with which to address
problems in robotics. This work introduces an approach to learning hybrid
systems from demonstrations, with an emphasis on extracting models that are
explicitly verifiable and easily interpreted by robot operators. We fit a
sequence of controllers using sequential importance sampling under a generative
switching proportional controller task model. Here, we parameterise controllers
using a proportional gain and a visually verifiable joint angle goal. Inference
under this model is challenging, but we address this by introducing an
attribution prior extracted from a neural end-to-end visuomotor control model.
Given the sequence of controllers comprising a task, we simplify the trace
using grammar parsing strategies, taking advantage of the sequence
compositionality, before grounding the controllers by training perception
networks to predict goals given images. Using this approach, we are
successfully able to induce a program for a visuomotor reaching task involving
loops and conditionals from a single demonstration and a neural end-to-end
model. In addition, we are able to discover the program used for a tower
building task. We argue that computer program-like control systems are more
interpretable than alternative end-to-end learning approaches, and that hybrid
systems inherently allow for better generalisation across task configurations
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