174 research outputs found
What's to Automate? A Task Analysis of AI-enabled Start-ups
Automation of tasks as a result of advances in Artificial Intelligence (AI) is currently one of the major economical drivers. However, the varying effectiveness of AI usage across occupations and industries suggests that the impact of AI diffusion is uneven. Thus, it is imperative to understand which types of tasks are more or less prevalent in AI-enabled businesses. Using a cross-sectional dataset of 27,700 start-ups and occupation data, we utilize word embedding to link start-ups to their respective underlying tasks. We compare the task types of AI-enabled with non-AI start-ups in the services and platforms domain using a suitability for machine learning metric. The results show that analytical, logistical, and statistical tasks predominate among AI-enabled start-ups while services with customer proximity have a smaller share and the overall task diversity is lower. The implications of our findings are discussed in the light of labor theory and the economies of scale of AI start-ups
Constrained Polynomial Zonotopes
We introduce constrained polynomial zonotopes, a novel non-convex set
representation that is closed under linear map, Minkowski sum, Cartesian
product, convex hull, intersection, union, and quadratic as well as
higher-order maps. We show that the computational complexity of the
above-mentioned set operations for constrained polynomial zonotopes is at most
polynomial in the representation size. The fact that constrained polynomial
zonotopes are generalizations of zonotopes, polytopes, polynomial zonotopes,
Taylor models, and ellipsoids, further substantiates the relevance of this new
set representation. The conversion from other set representations to
constrained polynomial zonotopes is at most polynomial with respect to the
dimension
Reachability Analysis of ARMAX Models
Reachability analysis is a powerful tool for computing the set of states or
outputs reachable for a system. While previous work has focused on systems
described by state-space models, we present the first methods to compute
reachable sets of ARMAX models - one of the most common input-output models
originating from data-driven system identification. The first approach we
propose can only be used with dependency-preserving set representations such as
symbolic zonotopes, while the second one is valid for arbitrary set
representations but relies on a reformulation of the ARMAX model. By analyzing
the computational complexities, we show that both approaches scale
quadratically with respect to the time horizon of the reachability problem when
using symbolic zonotopes. To reduce the computational complexity, we propose a
third approach that scales linearly with respect to the time horizon when using
set representations that are closed under Minkowski addition and linear
transformation and that satisfy that the computational complexity of the
Minkowski sum is independent of the representation size of the operands. Our
numerical experiments demonstrate that the reachable sets of ARMAX models are
tighter than the reachable sets of equivalent state space models in case of
unknown initial states. Therefore, this methodology has the potential to
significantly reduce the conservatism of various verification techniques.Comment: \copyright 2023 IEEE. Personal use of this material is permitted.
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Backward Reachability Analysis of Perturbed Continuous-Time Linear Systems Using Set Propagation
Backward reachability analysis computes the set of states that reach a target
set under the competing influence of control input and disturbances. Depending
on their interplay, the backward reachable set either represents all states
that can be steered into the target set or all states that cannot avoid
entering it -- the corresponding solutions can be used for controller synthesis
and safety verification, respectively. A popular technique for backward
reachable set computation solves Hamilton-Jacobi-Isaacs equations, which scales
exponentially with the state dimension due to gridding the state space. In this
work, we instead use set propagation techniques to design backward reachability
algorithms for linear time-invariant systems. Crucially, the proposed
algorithms scale only polynomially with the state dimension. Our numerical
examples demonstrate the tightness of the obtained backward reachable sets and
show an overwhelming improvement of our proposed algorithms over
state-of-the-art methods regarding scalability, as systems with well over a
hundred states can now be analyzed.Comment: 16 page
Formal Verification of Robotic Contact Tasks via Reachability Analysis
Verifying the correct behavior of robots in contact tasks is challenging due
to model uncertainties associated with contacts. Standard methods for testing
often fall short since all (uncountable many) solutions cannot be obtained.
Instead, we propose to formally and efficiently verify robot behaviors in
contact tasks using reachability analysis, which enables checking all the
reachable states against user-provided specifications. To this end, we extend
the state of the art in reachability analysis for hybrid (mixed discrete and
continuous) dynamics subject to discrete-time input trajectories. In
particular, we present a novel and scalable guard intersection approach to
reliably compute the complex behavior caused by contacts. We model robots
subject to contacts as hybrid automata in which crucial time delays are
included. The usefulness of our approach is demonstrated by verifying safe
human-robot interaction in the presence of constrained collisions, which was
out of reach for existing methods.Comment: This work has been accepted by the 22nd IFAC World Congress (2023 in
Yokohama, Japan
Human-Robot Gym: Benchmarking Reinforcement Learning in Human-Robot Collaboration
Deep reinforcement learning (RL) has shown promising results in robot motion
planning with first attempts in human-robot collaboration (HRC). However, a
fair comparison of RL approaches in HRC under the constraint of guaranteed
safety is yet to be made. We, therefore, present human-robot gym, a benchmark
for safe RL in HRC. Our benchmark provides eight challenging, realistic HRC
tasks in a modular simulation framework. Most importantly, human-robot gym
includes a safety shield that provably guarantees human safety. We are,
thereby, the first to provide a benchmark to train RL agents that adhere to the
safety specifications of real-world HRC. This bridges a critical gap between
theoretic RL research and its real-world deployment. Our evaluation of six
environments led to three key results: (a) the diverse nature of the tasks
offered by human-robot gym creates a challenging benchmark for state-of-the-art
RL methods, (b) incorporating expert knowledge in the RL training in the form
of an action-based reward can outperform the expert, and (c) our agents
negligibly overfit to training data
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