129 research outputs found
Safe Control Algorithms Using Energy Functions: A Unified Framework, Benchmark, and New Directions
Safe autonomy is important in many application domains, especially for
applications involving interactions with humans. Existing safe control
algorithms are similar to one another in the sense that: they all provide
control inputs to maintain a low value of an energy function that measures
safety. In different methods, the energy function is called a potential
function, a safety index, or a barrier function. The connections and relative
advantages among these methods remain unclear. This paper introduces a unified
framework to derive safe control laws using energy functions. We demonstrate
how to integrate existing controllers based on potential field method, safe set
algorithm, barrier function method, and sliding mode algorithm into this
unified framework. In addition to theoretical comparison, this paper also
introduces a benchmark which implements and compares existing methods on a
variety of problems with different system dynamics and interaction modes. Based
on the comparison results, a new method, called the sublevel safe set
algorithm, is derived under the unified framework by optimizing the
hyperparameters. The proposed algorithm achieves the best performance in terms
of safety and efficiency on the vast majority of benchmark tests.Comment: This is the extended version of a paper submitted to 58th Conference
on Decision and Control March, 2019; revised August, 201
Online Verification of Deep Neural Networks under Domain or Weight Shift
Although neural networks are widely used, it remains challenging to formally
verify the safety and robustness of neural networks in real-world applications.
Existing methods are designed to verify the network before use, which is
limited to relatively simple specifications and fixed networks. These methods
are not ready to be applied to real-world problems with complex and/or
dynamically changing specifications and networks. To effectively handle
dynamically changing specifications and networks, the verification needs to be
performed online when these changes take place. However, it is still
challenging to run existing verification algorithms online. Our key insight is
that we can leverage the temporal dependencies of these changes to accelerate
the verification process, e.g., by warm starting new online verification using
previous verified results. This paper establishes a novel framework for
scalable online verification to solve real-world verification problems with
dynamically changing specifications and/or networks, known as domain shift and
weight shift respectively. We propose three types of techniques (branch
management, perturbation tolerance analysis, and incremental computation) to
accelerate the online verification of deep neural networks. Experiment results
show that our online verification algorithm is up to two orders of magnitude
faster than existing verification algorithms, and thus can scale to real-world
applications
Multimodal Safe Control for Human-Robot Interaction
Generating safe behaviors for autonomous systems is important as they
continue to be deployed in the real world, especially around people. In this
work, we focus on developing a novel safe controller for systems where there
are multiple sources of uncertainty. We formulate a novel multimodal safe
control method, called the Multimodal Safe Set Algorithm (MMSSA) for the case
where the agent has uncertainty over which discrete mode the system is in, and
each mode itself contains additional uncertainty. To our knowledge, this is the
first energy-function-based safe control method applied to systems with
multimodal uncertainty. We apply our controller to a simulated human-robot
interaction where the robot is uncertain of the human's true intention and each
potential intention has its own additional uncertainty associated with it,
since the human is not a perfectly rational actor. We compare our proposed safe
controller to existing safe control methods and find that it does not impede
the system performance (i.e. efficiency) while also improving the safety of the
system
Kinetic and thermodynamic analysis of ultra-high pressure and heat-induced denaturation of bovine serum albumin by surface plasmon resonance
Purpose: To undertake comparative kinetic and thermodynamic analyses of the interaction of bovine serum albumin (BSA) with IgG pre-treated with ultra-high pressure (UHP) and moderate heat.Methods: BSA solutions were processed at 100 – 600 MPa and 25 – 40 °C. We applied an optical biosensor based on surface plasmon resonance (SPR). The dissociation and association kinetics of antigen-antibody complexes were measured at different temperatures. By analyzing the resultant sensograms, the association rate constant (ka), dissociation rate constant (kd), equilibrium dissociation constant (KD), and thermodynamic parameters were calculated.Results: The equilibrium disassociation constant, KD, ranged from a low value of 3.15 × 10−7 M (0.1 MPa, 25 °C) to a high value of 66.42 × 10−7 M (600 MPa, 55 °C). Increase in pressure and temperature led to decrease in the affinity of BSA for IgG. Pressure levels above 300 MPa promoted interactions between breakage of disulfide bonds, and the unfolding and aggregation of BSA.Conclusions: These results show that the combination of UHP and moderate heat treatment cdecrease the allergenicity of BSA by changing their protein conformation.Keywords: Ultra - high pressure, Bovine serum albumin, Surface plasmon resonance, Kinetics, Thermodynamics, Allergen
Toward Unbiased Multiple-Target Fuzzing with Path Diversity
In this paper, we propose a novel directed fuzzing solution named AFLRun,
which features target path-diversity metric and unbiased energy assignment.
Firstly, we develop a new coverage metric by maintaining extra virgin map for
each covered target to track the coverage status of seeds that hit the target.
This approach enables the storage of waypoints into the corpus that hit a
target through interesting path, thus enriching the path diversity for each
target. Additionally, we propose a corpus-level energy assignment strategy that
guarantees fairness for each target. AFLRun starts with uniform target weight
and propagates this weight to seeds to get a desired seed weight distribution.
By assigning energy to each seed in the corpus according to such desired
distribution, a precise and unbiased energy assignment can be achieved.
We built a prototype system and assessed its performance using a standard
benchmark and several extensively fuzzed real-world applications. The
evaluation results demonstrate that AFLRun outperforms state-of-the-art fuzzers
in terms of vulnerability detection, both in quantity and speed. Moreover,
AFLRun uncovers 29 previously unidentified vulnerabilities, including 8 CVEs,
across four distinct programs
Safety Index Synthesis via Sum-of-Squares Programming
Control systems often need to satisfy strict safety requirements. Safety
index provides a handy way to evaluate the safety level of the system and
derive the resulting safe control policies. However, designing safety index
functions under control limits is difficult and requires a great amount of
expert knowledge. This paper proposes a framework for synthesizing the safety
index for general control systems using sum-of-squares programming. Our
approach is to show that ensuring the non-emptiness of safe control on the safe
set boundary is equivalent to a local manifold positiveness problem. We then
prove that this problem is equivalent to sum-of-squares programming via the
Positivstellensatz of algebraic geometry. We validate the proposed method on
robot arms with different degrees of freedom and ground vehicles. The results
show that the synthesized safety index guarantees safety and our method is
effective even in high-dimensional robot systems
ADD: An Automatic Desensitization Fisheye Dataset for Autonomous Driving
Autonomous driving systems require many images for analyzing the surrounding
environment. However, there is fewer data protection for private information
among these captured images, such as pedestrian faces or vehicle license
plates, which has become a significant issue. In this paper, in response to the
call for data security laws and regulations and based on the advantages of
large Field of View(FoV) of the fisheye camera, we build the first Autopilot
Desensitization Dataset, called ADD, and formulate the first
deep-learning-based image desensitization framework, to promote the study of
image desensitization in autonomous driving scenarios. The compiled dataset
consists of 650K images, including different face and vehicle license plate
information captured by the surround-view fisheye camera. It covers various
autonomous driving scenarios, including diverse facial characteristics and
license plate colors. Then, we propose an efficient multitask desensitization
network called DesCenterNet as a benchmark on the ADD dataset, which can
perform face and vehicle license plate detection and desensitization tasks.
Based on ADD, we further provide an evaluation criterion for desensitization
performance, and extensive comparison experiments have verified the
effectiveness and superiority of our method on image desensitization
State-wise Constrained Policy Optimization
Reinforcement Learning (RL) algorithms have shown tremendous success in
simulation environments, but their application to real-world problems faces
significant challenges, with safety being a major concern. In particular,
enforcing state-wise constraints is essential for many challenging tasks such
as autonomous driving and robot manipulation. However, existing safe RL
algorithms under the framework of Constrained Markov Decision Process (CMDP) do
not consider state-wise constraints. To address this gap, we propose State-wise
Constrained Policy Optimization (SCPO), the first general-purpose policy search
algorithm for state-wise constrained reinforcement learning. SCPO provides
guarantees for state-wise constraint satisfaction in expectation. In
particular, we introduce the framework of Maximum Markov Decision Process, and
prove that the worst-case safety violation is bounded under SCPO. We
demonstrate the effectiveness of our approach on training neural network
policies for extensive robot locomotion tasks, where the agent must satisfy a
variety of state-wise safety constraints. Our results show that SCPO
significantly outperforms existing methods and can handle state-wise
constraints in high-dimensional robotics tasks.Comment: arXiv admin note: text overlap with arXiv:2305.1368
- …