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Semi-supervised learning improves the performance of supervised machine learning by leveraging methods  from  unsupervised  learning  to  extract  information  not  explicitly  available  in  the  labels.Through the design of a system that enables a robot to learn inspection strategies from a human operator, we present a hybrid semi-supervised system capable of learning interpretable and verifiable models from demonstrations.  The system induces a controller program by learning from immersive demonstrations using sequential importance sampling.  These visual servo controllers are parametrised  by  proportional  gains  and  are  visually  verifiable  through  observation  of  the  robot’s position in the environment.  Clustering and effective particle size filtering allows the system to dis-cover goals in the state space. These goals are used to label the original demonstration for end-to-end learning of behavioural models.  The behavioural models are used for autonomous model predictive control and scrutinised for explanations. We implement causal sensitivity analysis to identify salient objects and generate counterfactual conditional explanations. These features enable decision making interpretation and post hoc discovery of the causes of a failure.  The proposed system expands on previous approaches to program synthesis by incorporating repellers in the attribution prior of the sampling process.  We successfully learn the hybrid system from an inspection scenario where an unmanned ground vehicle has to inspect, in a specific order, different areas of the environment. The system induces an interpretable computer program of the demonstration that can be synthesised to produce novel inspection behaviours.  Importantly, the robot successfully runs the synthesised pro-gram on an unseen configuration of the environment while presenting explanations of its autonomous behaviour.

ECAI 2020

Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study

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Dear Colleagues and Friends,

The 33rd IEEE International Conference on Robot and Human Interactive Communication, IEEE RO-MAN 2024, was held at Pasadena Convention Center in Pasadena, USA.

The theme of the IEEE RO-MAN 2024 was **“Embracing Human-Centered HRI”**.

With the continuous technological effort of understanding human needs, creating human-desired robotic systems, and developing real human user experiences, we are marching together towards building a promising and excellent human quality of life, all for our wellness and our next generation.

The conference provided a forum for researchers and practitioners to discuss a variety of topics related to the broad theme of Robot and Human Interactive Communication involving theories, methodologies, technologies, empirical and experimental studies, and generated more insights for the future of Robot and Human Interactive Communication!

We hope you continue to enjoy the post-conference content on Underline!
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IEEE RO-MAN 2024

The 33rd IEEE International Conference on Robot and Human Interactive Communication was held in Pasadena, California, USA. The theme of the IEEE RO-MAN 2024 was “Embracing Human-Centered HRI”.

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Automated machine learning (AutoML) systems aim at finding the best machine learning (ML) pipeline that automatically matches the task and data at hand. We investigate the robustness of machine learning pipelines generated with three AutoML systems, TPOT, H2O, and AutoKeras. In particular, we study the influence of dirty data on accuracy, and consider how using dirty training data may help create more robust solutions. Furthermore, we also analyze how the structure of the generated pipelines differs in different cases.

Testing the Robustness of AutoML Systems

"Human-Robot Collaboration (HRC) is rapidly replacing the traditional application of robotics in the manufacturing industry. Robots and human operators no longer have to perform their tasks in segregated areas and are capable of working in close vicinity and performing hybrid tasks---performed partially by humans and by robots.

We have presented a methodology in an earlier work to promote and facilitate formally modeling HRC systems, which are notoriously safety-critical. Relying on temporal logic modeling capabilities and automated model checking tools, we built a framework to formally model HRC systems and verify the physical safety of human operator against ISO 10218-2 standard. In order to make our proposed formal verification framework more appealing to safety engineers, whom are usually not very fond of formal modeling and verification techniques, we decided to couple our model checking approach with a 3D simulator that demonstrates the potential hazardous situations to the safety engineers in a more transparent way. This paper reports our co-simulation approach, using Morse simulator and Zot model checker."

Co-Simulation of Human-Robot Collaboration: from Temporal Logic to 3D Simulation

Given the complexity of real-world, unstructured domains, it is often impossible or impractical to design models that include every feature needed to handle all possible scenarios that an autonomous system may encounter. For an autonomous system to be reliable in such domains, it should have the ability to improve its competence online. In this paper, we propose a method for improving the competence of a system over the course of its deployment. We specifically focus on a class of semi-autonomous systems known as competence-aware systems that model their own competence---the optimal extent of autonomy to use in any given situation---and learn this competence over time from feedback received through interactions with a human authority. Our method exploits such feedback to identify important state features missing from the system's initial model, and incorporates them into its state representation. The result is an agent that better predicts human involvement, leading to improvements in its competence and reliability, and as a result, its overall performance.

Improving Competence for Reliable Autonomy

"Autonomous systems developed with the Belief-Desire-Intention (BDI) architecture are usually mostly implemented in simulated environments. In this project we sought to build a BDI agent for use in the real world for campus mail delivery in the tunnel system at Carleton University.  Ideally, the robot should receive a delivery order via a mobile application, pick up the mail at a station, navigate the tunnels to the destination station, and notify the recipient. We linked the Robot Operating System (ROS) with a BDI reasoning system to achieve a subset of the required use cases. ROS handles the low-level sensing and actuation, while the BDI reasoning system handles the high-level reasoning and decision making. Sensory data is orchestrated and sent from ROS to the reasoning system as perceptions. These perceptions are then deliberated upon, and an action string is sent back to ROS for interpretation and driving of the necessary actuator for the action to be performed.

In this paper we present our current implementation, which closes the loop on the hardware-software integration, and implements a subset of the use cases required for the full system."

Toward Campus Mail Delivery using BDI

Knowing when a driver will quit cruising and either leave the area or park at an expensive off-street facility is critical for modeling parking search. We employ a serious game – PARKGAME for estimating the dynamics of drivers’ decision making. 49 Participants of a game experiment were involved in three scenarios where they had to arrive on time to a fictional appointment or face monetary penalties, and to choose between uncertain but cheap on-street parking or a certain but costly parking lot. Scenarios diverged on the time to appointment and distance between the meeting place and parking lot locations. Players played a series of 8 or 16 computer games on a Manhattan grid road network with high on-street parking occupancy and nearby parking lot of unlimited capacity. Players’ choices to quit or to continue search, as dependent on the search time, were analyzed with an accelerated-failure time (AFT) model. Results show that drivers are mostly risk-averse and quit on-street parking search very soon after potential loses begin to accumulate. The implications of game-based methods for simulation model development and sustainable parking policy are further discussed.

To quit or to cruise? Modeling parking search decisions in serious games

Enabling Ride-sharing (RS) in existing Mobility-on-demand (MoD) systems allows to reduce the operating vehicle fleet size while achieving a similar level of service. This however requires an efficient vehicle to multiple requests assignment, which is the focus of most RS-related research, and an adaptive fleet rebalancing strategy, which counteracts the uneven geographical spread of demand and relocates unoccupied vehicles to the areas of higher demand. Existing research into rebalancing generally divides the system coverage area into predefined geographical zones, however, this is done statically at design-time and can limit their adaptivity to evolving demand patterns. To enable dynamic, and therefore more accurate rebalancing, this paper proposes a Dynamic Demand-Responsive Rebalancer (D2R2) for RS systems. D2R2 uses Expectation-Maximization (EM) clustering to determine relocation zones at runtime. D2R2 recalculates zones at each decision step and assigns them relative probabilities based on current demand. We demonstrate the use of D2R2 by integrating it with a Deep Reinforcement Learning multi-agent RS-enabled MoD system in a fleet of 200 vehicle agents serving 10,000 trips extracted from New York taxi trip data. Results show a more fair workload division across the fleet without loss of performance with respect to waiting time and distribution of passengers per vehicle, when compared to baselines with no rebalancing and static pre-defined equiprobable zones.

Demand-Responsive Zone Generation for Real-Time Vehicle Rebalancing in Ride-Sharing fleets

Reinforcement learning is an efficient, widely used machine learning technique that performs well when the state and action spaces are reasonable. This is rarely the case regarding control-related problems, as for instance controlling traffic signals. Here, the state space can be very large. In order to deal with the curse of dimensionality, a rough discretization of such space can be used but this is effective just up to a certain point. A way to mitigate this is to use techniques that generalize the state space such as function approximation. In this paper, a linear function approximation is used. Specifically, SARSA(λ) with  Fourier basis features is implemented to control traffic signals in the agent-based transport simulation MATSim. The results are compared not only to trivial controllers such as fixed-time, but also to state-of-the-art rule-based adaptive methods. It is concluded that SARSA(λ) with Fourier basis features is able to outperform such methods, especially in scenarios with varying traffic demands.

A reinforcement learning approach with Fourier basis linear function approximation for traffic signal control

"* Université Lumière Lyon 2;
roua.elchamaa@univ-lyon2.fr;
yacine.ouzrout@univ-lyon2.fr;
† Saint-Joseph University (ESIB);
rima.kilany@usj.edu.lb;
;
‡ Univ. Lyon, University Claude;
Bernard Lyon 1;
France Email: baudouin.dafflon@univ-lyon1.fr;"

Semantic Traffic Data Analysis for a Local Leader Election Algorithm (LLEA)

Travel apps become more and more popular giving information about the current traffic state to drivers who then adapt their route choice. In commuting scenarios, where people repeatedly travel between a particular origin and destination, learning effects add to this information. In this paper, we analyse the effects on the overall network, if adaptive driver agents share their aggregated experience about route choice in a reinforcement learning (Q-learning) setup. Drivers share what they have learnt about the system, not just information about their current travel times. We can show in a standard scenario that experience sharing can improve convergence times for adaptive driver agents.

Experience Sharing in a Traffic Scenario

"On-Demand Transport (ODT) systems have attracted increasing attention in recent years. 
  Traditional centralized dispatching can achieve optimal solutions with NP-Hard complexity, making it unsuitable for online and dynamic problems. Decentralized approaches can achieve fast feasible solution at run-time with no guarantee on the quality. Starting from feasible not optimal solution, we present in this work a new decentralized, insertion heuristic algorithm to solve a special case of the dynamic Dial-A-Ride-Problem (DARP)  as an ODT system, in which vehicles communicate via Vehicle-to-vehicle communication (V2V)  and take decentralized decisions. In our solution model we add an optimization phase in order to improve the quality  of global solution achieved by decentralized decision at run-time by exchanging resources between vehicles. We evaluate and analyze the promising results of our contributed technique on synthetic data for taxis operating in Saint-Étienne city, against a classical greedy approach."

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