United States

This project focuses on robust gait development using reinforcement learning on a 18-DOF hexapod
platform. Each actuator is equipped with a control strategy that sums position, velocity, and effort PID
control to produce motor PWM output. Due to the underlying framework, low level access to the
actuators is limited and the interface for communication is in the form of position, velocity, and torque
joint commands. The goal of this project is to create a simulation environment that can be used to train
the hexapod to walk using a soft actor-critic policy with a custom reward function. Using existing joint
feedback data from an established gait, the simulation model is enhanced to include control gains,
positional joint limits, and torque limits. Once the simulation is deemed to be high-fidelity, the soft
actor-critic policy is used to train a forward-walking gait in simulation using a tuned reward function.
The model produced by this learning event can be analyzed to determine the effectiveness of this gait
based on certain criterion: forward velocity, lateral velocity error, stance height, and balance. If the
learned gait is categorized as viable based on the aforementioned criterion, the model is then tested on
the physical robot. The learned model produces joint level commands based on joint feedback received
from the actuators.
The challenge of breaking down the gap between the simulation and real environments is explored in
this presentation. The MuJoCo environment used for simulating the robot is limited in its capabilities
for modeling individual actuators. Rather than specifying function-based control schema, a user is
given the ability to modify damping, armature, friction, and joint limit parameters. Identifying the
appropriate simulation parameters required thorough analysis of the existing control scheme aboard the
physical robot and relating it to the tunable parameters within the simulation environment. This proved
difficult due to the complex dynamics of the 18-DOF system. After much trial and error, suitable
simulation environment parameters were identified and a learned model was produced that yielded a
forward gait. The current model produces a gait with forward velocity of ~0.7 m/s, but future work
includes tuning the reward function to derive a gait with lesser forward velocity due to instability found
in testing on the physical robot.

NAML 2024

Hexapod Gait Optimization Utilizing Reinforcement Learning

hexapod

gait

reinforcement learning

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In case you need it urgently please contact: francesca.gasparini@unimib.it. 

 

The 20th International Conference of the Italian Association for Artificial Intelligence (**AIxIA 2021**) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

 
 



**General Chair**

Stefania Bandini, University of Milano-Bicocca, Italyand RCAST - The University of Tokyo, Japan


**Program Chairs**

Viviana Mascardi, University of Genova, Italy; Matteo Palmonari, University of Milano-Bicocca, Italy; Giuseppe Vizzari, University of Milano-Bicocca, Italy

**Organizing Committee**

Francesca Gasparini, University of Milano-Bicocca, Italy (**Chair**); Stefano Ferilli, University of Bari, Italy; Marta Giltri, University of Milano-Bicocca, Italy; Daniela Briola, University of Insubria, Italy


**Publicity and Communication Committee**

Elisabetta Fersini, University of Milano-Bicocca, Italy (**Chair**); Angelo Ferrando, University of Genova, Italy; Francesca Alessandra Lisi, University of Bari, Italy; Giovanni Stilo, University of L'Aquila, Italy; Marco Viviani, University of Milano-Bicocca, Italy


**Workshop Chairs**

Gabriella Cortellessa, ISTC-CNR, Rome, Italy; Silvia Rossi, University of Naples Federico II, Italy; Fabio Stella, University of Milano-Bicocca, Italy


**PC Members**

Fulvio Mastrogiovanni, University of Genova, Italy; Roberto Basili, University of Rome Tor Vergata, Italy; Nicola Gatti, Polytechnic University of Milan, Italy; Stefano Bistarelli, University of Perugia, Italy; Piero Poccianti, Consorzio Operativo Gruppo MPS, Italy; Francesco Scarcello, University of Calabria, Italy; Flavio S. Correa Da Silva, University of São Paulo, Brazil; Elena Cabrio, Université Côte d'Azur, CNRS, Inria, I3S, France; Antonio Lieto, University of Turin, Italy; Diego Calvanese, Free University of Bozen-Bolzano, Italy; Alessio Micheli, University of Pisa, Italy; Francesca A. Lisi University of Bari Aldo Moro, Italy; Mauro Vallati, University of Huddersfield, United Kingdom; Bernardo Magnini, Fondazione Bruno Kessler (FBK), Italy; Luigia C. Aiello, Sapienza University of Rome, Italy; Rafael H. Bordini, Pontifical Catholic University of Rio Grande do Sul, Brazil; Chiara Ghidini, Fondazione Bruno Kessler (FBK), Italy; Federico Bergenti, University of Parma, Italy; Francesco Ricca, University of Calabria, Italy; Andrea Burattin, Technical University of Denmark, Denmark; Angelo Oddi, ISTC-CNR, Italian National Research Council, Italy; Farshad Badie, Aalborg University, Denmark; Emanuel Sallinger, University of Oxford, United Kingdom; Marin Lujak, University Rey Juan Carlos, Spain; Giancarlo Fortino, University of Calabria, Italy; Salvatore Gaglio, University of Palermo, Italy; Johannes K. Fichte, TU Dresden, Germany; Matteo Baldoni, University of Turin, Italy; Fabrizio Riguzzi, Univeristy of Ferrara, Italy; Ivana Dusparic, Trinity College Dublin, Ireland; Serena Villata, CNRS - Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis, France; Paolo Rosso, Technical University of Valencia, Spain; Ana L. C. Bazzan, Federal University of Rio Grande do Sul, Brazil; Gianluigi Greco, University of Calabria, Italy; Stefano Cagnoni, University of Parma, Italy; Gian Luca Pozzato, University of Turin, Italy; Salvatore Ruggieri, University of Turin, Italy; Claudio Di Ciccio, Sapienza University of Rome, Italy; Eloisa Vargiu, Cetaqua, Water Technology Center, Spain; Marco Maratea, University of Genova, Italy; Andrea Roli, University of Bologna, Italy; Viviana Patti, University of Turin, Italy; Stefania Costantini, University of L'Aquila, Italy; Antonius Weinzierl, Vienna University of Technology, Austria; Silvia Rossi, University of Naples Federico II, Italy; Giovanni Semeraro, University of Bari, Italy; Luciano Serafini, Fondazione Bruno Kessler (FBK), Italy; Laura Giordano, University of Eastern Piedmont, Italy; Ernesto Jimenez-Ruiz, City, University of London, United Kingdom; Marco Gavanelli, University of Ferrara, Italy; Federico Chesani, University of Bologna, Italy

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AIxIA 2021

The 20th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2021) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

technical paper

### Welcome to the 8th annual workshop on Naval Applications of Machine Learning!

NAML is the premier annual event showcasing current machine learning research relevant to Naval applications. We welcome participation from the defense community, industry, and academia. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

**General Chairs:**

Katie Rainey, Naval Information Warfare Center Pacific

Nicole Isoda, Naval Information Warfare Center Pacific 

**[More about NAML 2024](https://sites.google.com/go.spawar.navy.mil/naml/)**

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The 8th annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.


When researching robot swarms, many studies observe complex group behavior emerging from the individual agents' simple local actions. 
However, the task of learning an individual policy to produce a desired emergent behavior remains a challenging and largely unsolved problem.
We present a method of training distributed robotic swarm algorithms to produce emergent behavior. Inspired by the biological evolution of emergent behavior in animals, we use an evolutionary algorithm to train a 'population' of individual behaviors to approximate a desired group behavior. 
We perform experiments using simulations of the Georgia Tech Miniature Autonomous Blimps (GT-MABs) aerial robotics platforms conducted in the CoppeliaSim simulator. 
Additionally, we test on simulations of Anki Vector robots to display our algorithm's effectiveness on various modes of actuation.
We evaluate our algorithm on various tasks where a somewhat complex group behavior is required for success. These tasks include an Area Coverage task, a Surround Target task, and a Wall Climb task. 
We compare behaviors evolved using our algorithm against designed policies, which we create in order to exhibit the emergent behaviors we desire.

Learning Emergent Behavior in Robot Swarms with NEAT

Teams of mobile [aerial, ground, or aquatic] robots have applications in delivering resources, patrolling, information-gathering, pesticide application to crops, forest fire fighting, chemical plume source localization and mapping, and search-and-rescue. Some environments contain hazards e.g., rough terrain or seas, strong winds, or adversaries capable of attacking robots. Then, the robots must coordinate their trails in the hazardous environment to (i) cooperatively achieve the team-level objective with robustness to robot failures and (ii) strike some balance between expected robot failures versus utility gained.

Herein, we pose the bi-objective, risky team orienteering problem, where (i) a team of robots are mobile within an environment, abstracted as a graph (nodes: locations, edges: spatial connections between locations); (ii) each node offers a reward to the team depending on the number of robots that visit it; (iii) the traversal of each edge in the graph imposes a risk of robot failure/destruction; and (iv) the two [often, conflicting] team objectives are to maximize the expected (a) team reward and (b) number of robots that survive the mission. We then use ant colony optimization to search for the Pareto-optimal set of robot team trail plans. A human decision-maker can then select the robot trail plans that balance, according to their values, the two [conflicting] objectives.

Bi-Objective Ant Colony Optimization of the Risky, Robot Team Orienteering Problem

Flying by Ear: Acoustic sensing for unmanned air vehicles in complex environments

In this talk, we will discuss a deep learning method for both detecting and localizing damage in metal plates. To this end, we analyze time-series data that reflects material properties; specifically, we use a square arrangement of piezoelectric transducers mounted to an aluminum sheet, one of which generates a vibrational wave that then traverses the region of interest before being received by the remaining three sensors. 
A key aspect of our model design is the incorporation of physical principles, namely, symmetries associated with both time and the square arrangement of sensors, in addition to recognizing that the relevant physics is characterized by a hierarchy of scales. That is to say, while it is well known that deep neural networks can monitor structural health, we address the question of optimal architecture by appealing to the geometric deep learning blueprint. 
In summary, we will discuss our efforts, results, and lessons learned in the context of developing a neural network to infer from material response measurements both the existence and location of damage.


Staying Alert to Fatigue: DL Enabled Detection and Localization of Damage in Metal Plates

We propose a novel method of analyzing dynamic
systems for chaotic behavior using machine learning
techniques. The study of chaos in dynamic systems often
involves analyzing visual representations of data points
generated by the systems. One method includes embedding
one-dimensional time-series data into a two-dimensional
space (a “pq diagram”) as an interim step in the test for
chaotic behavior. We study the application of a machine
learning visual recognition model —a Convolutional Neural
Network— on the pq diagrams generated in various systems
and determine that they can be used to reliably differentiate
chaotic and periodic systems from one another, and possibly
to differentiate between different types of chaotic systems
themselves.

Detection and Discrimination of Chaotic Behavior Using Visual Recognition Models

The modeling of complex chemical kinetics in modern physics simulations are increasingly hampered by the incredible computational costs required to perform them. These sorts of simulations, such as combustion models, find that as the number of involved chemical species increase, so does the percentage of total compute time required by the chemical kinetics modeling alone. The computational cost often finds that the most advanced chemistry models to be intractable on even the most advanced computing systems.

To overcome this limitation, modern machine learning solutions can be employed to replace the embedded chemical kinetics solvers found in simulation codes. As machine learning continues to advance at break-neck speeds, their integration among existing simulations can still prove tricky as the additional programming or framework overheads can make them less attractive than simply sticking to the tried-and-true solvers already in use.

By using methods to convert the machine learning models into native code that directly aligns to the simulation already in use, some or most of this use can be avoided, and the code that is generated has the opportunity to be tuned to precisely benefit the physics simulation that is intended to be helped. This method of code generation of machine learning models allows the benefits of modern ML without requiring extra libraries and programming frameworks that can sometimes prove tedious to include. This capability present a hopeful new way to integrate and improve existing chemical kinetics solvers in the on-going physics simulations that use them today.


Code Generation of Advanced Machine Learning Models for Chemical Kinetics

Mesh characteristics and mesh quality are important inputs in thermodynamic and fluid simulations. 
Currently, limited or naïve processes are used to determine the quality of an existing mesh before and after repair. 
Meshes are considered “good” based on appearance and lack of those geometric defects that impact analysis. 
There is a need for robust metrics to characterize the quality of a mesh. 
These metrics can be used to evaluate the mesh locally (for application specific analysis) and globally (to compare various versions of the same model). 
We are exploring the use of Topological Data Analysis (TDA) methods, Geometry-based algorithms, and Boolean model comparisons 
as indicators for a “good” or “bad” 3D Computer-Aided Design (CAD) model meshes. We have developed a series of robust CAD mesh metrics 
to characterize the quality of a mesh. These metrics are applied methodically to re-mesh and repair 3D CAD models, 
including increasing and decreasing the number of facets, collapse nodes, edges, faces (decimating), and watertighting.
These methods have been implemented in a semi-automated workflow, requiring a human-in-the-loop on in determining which values best suite a given applicaiton.

Mesh characteristics and mesh quality are important inputs in thermodynamic and fluid simulations. Currently, limited or naïve processes are used to determine the quality of an existing mesh before and after repair. Meshes are considered “good” based on appearance and lack of those geometric defects that impact analysis. There is a need for robust metrics to characterize the quality of a mesh. These metrics can be used to evaluate the mesh locally (for application specific analysis) and globally (to compare various versions of the same model). We are exploring the use of Topological Data Analysis (TDA) methods, Geometry-based algorithms, and Boolean model comparisons as indicators for a “good” or “bad” 3D Computer-Aided Design (CAD) model meshes. We have developed a series of robust CAD mesh metrics to characterize the quality of a mesh. These metrics are applied methodically to re-mesh and repair 3D CAD models, including increasing and decreasing the number of facets, collapse nodes, edges, faces (decimating), and watertighting. These methods have been implemented in a semi-automated workflow, requiring a human-in-the-loop on in determining which values best suite a given applicaiton.



Mesh metrics for simplified, feature-preserving CAD models

Introduction: Approximately 1 out of 4 Navy recruits will not complete their obligated service. Identifying factors influencing Navy attrition versus retention is pivotal for maintaining a skilled and motivated force. This study utilizes machine learning and variable importance measures to identify risk factors that contribute to Navy attrition versus retention. Variable importance measures are extracted from random forest and extreme gradient boosting decision trees among newly recruited Navy accessions. These classification tree methods are more robust and sensitive when dealing with several hundreds of predictor variables, offering superior performance relative to traditional multivariable analyses. Leveraging advanced analytics is crucial to better understand these factors for military organizations. Navy retention refers to the rate at which military personnel choose to continue their service beyond their initial enlistment period.

Methods: The inclusion criterion was Navy personnel with an accession date in 2016 and the outcome of interest was a discharge date before the end of obligated service (i.e., attrition). The analysis focused on 545 independent variables branched from demographics, medical outpatient visits, and outpatient pharmacy medications. Accession, discharge, and demographic information were obtained from the Career History Archival and Medical Personnel System. The medical and pharmacy outpatient data were obtained from the Military Health System Medical Data Repository. All research and study materials were conducted under IRB protocol NRHC.2022.2020 and followed all HIPAA and human subject research guideline and protocols.
.
Results: Random forest (RF), extreme gradient boosting (XGBoost), and logistic regression (LR) models were trained with different subsets of features to determine which set of variables provided the best prediction for attrition. Model performance metrics were generated describing the sensitivity, specificity, positive predictive value and negative predictive value, area under the curve, and classification accuracy for the validation data. Variable importance measures (VIM) were then assessed including mean decrease accuracy, mean decrease Gini impurity, gain, and cover. The resulting VIM identified 5 groups: mental health, alcohol related problems, occupations, gender, musculoskeletal and medical appointment/prescription compliance. The top 3 VIM and their overall relative risk on attrition were adjustment disorders (RR=1.39 higher), Seaman Specialists (RR=3.01 higher) and Electronic Equipment Repairers (RR=.44 lower). 
Among Navy accessions in 2016 (n=39,866), 28.15% (n=11,177) did not complete their obligated service. The best overall accuracy for predicting attrition and AUC was 81.7% and 90.0% from the RF model respectively. Both the RF and XGBoost models outperformed the LR.

Conclusion: This study demonstrates the effectiveness of variable importance measures as a valuable tool in decision-making processes within the context of the military personnel management and retention system. It also demonstrates the utility of machine learning models and the benefits when compared to traditional regression analyses. Ensemble approaches improve overall predictive performance and makes the model more robust and resistant to overfitting compared to a single decision tree.


A Comparison of Variable Importance Measures in Predicting Navy Retention Among Navy Recruits in 2016

Opponent surprise should be considered a common occurrence in war. This research makes recommendations to ensure an AI provides realistic solutions. Consider a non-combinatorial version of chess where unknowns are introduced sometime during the game in favor of the opponent. For example, the opposing player’s pawn can move two squares forward multiple times. Adaptive algorithms, adjusting tactics during non-stationary game play, have been developed as a solution when time invariant novelty or emergent scenarios occur. Now consider a worst case where every several moves, the opponent is allowed to use his game pieces in surprising ways, where change is now time variant. The research offers an approach to maintain user trust through XAI techniques and describes a mathematical foundation to avoid poor recommendations and uncover emergent solutions to uncover confounder. Use of game trees and payoff tables, Event-Verb-Event chains of causal sequences, and Directed Acyclic Graph models introduce a do-calculus inspired backdoor weight adjustment and open world error contributions theorem explaining confounder bias and bounding the algorithm’s recommendation error. The research offers dataset design constructs that provide the algorithm with the ability to provide limitations to algorithm recommendations due to confounder bias and explain confounder error contributions within the recommendation. The design constructs result in a larger dataset size specific to encountering and more effectively handling novelty during game play, allowing for more realistic explanations to better maintain user trust and compensate for confounders in the algorithm’s recommended solutions.

AI dataset design recommendations to deal with unknowns

One of the biggest Navy challenges in fleet management is the security and assurance of software and hardware components being loaded or updated when ships come to port. Ship components that may otherwise be inaccessible or out of band may temporarily be connected through the boarding of maintenance staff that may have both physical and network access of critical components and/or through wireless updates and transmissions. Security and attestation ML tools that perform detection of anomalies are limited by the availability and quality of data.

Oracle will present topics in two areas that are critical in developing effective ML tools in response to outlined Navy challenges. The first is in establishing a digital twin environment in which complex engineering assets are represented by a “Golden System” based on thoroughly tested components. A digital twin is a virtual copy of a real-world asset, augmented by real-time data and analytics. The Golden System (i.e., digital twin) is used to enable the training of ML algorithms that perform critical functions such as anomaly detection by generating a baseline of telemetry, time series, and signatures. The second topic pertains to data quality through improved data sampling and preprocessing.

Oracle has developed Intelligent Data Preprocessing (IDP) innovations to optimize ML performance for a variety of Machine Learning use cases and data mining techniques. Use cases include prognostics, streaming analytics, prognostic cyber security, and EMI fingerprints for detection of counterfeit components in enterprise data center IT assets. The IDP innovations include Analytical Resampling Process (ARP), Unquantize (Unquantization of Quantized Sensor Signals), and Missing Value Imputation (MVI). The IDP algorithms have been integrated into a workflow referred to as a Data Preprocessing Pipeline (DPP). The use of the DPP has resulted in higher prognostic accuracy, and lower false-alarm and missed alarm probabilities in a variety of ML algorithm-based models. DPP packages are implemented in Python and MATLAB.

Oracle will present an extension of the concept of digital twins as applied to EMI fingerprinting to passively detect counterfeit components in electronic systems (in reference to hardware assurance and attestation challenges). A golden system is used to create a trusted signature. This signature is based on CPU activity from which EMI telemetry is derived and stored. Oracle’s Multivariate State Estimation Technique (MSET) 2 is used to detect counterfeit components by referencing the golden system derived signature. A research and development pilot is underway with an Oracle Government Defense and Intelligence (GDI) customer to demonstrate MSET2 EMI Anomaly detection above current innovation standard capabilities.

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Learning Emergent Behavior in Robot Swarms with NEAT

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