New Zealand

Erik Billing, Thomas Hellström and Lars-Erik Janlert (February 3rd 2012). Robot Learning from Demonstration Using Predictive Sequence Learning, Robotic Systems - Applications, Control and Programming, Ashish Dutta, IntechOpen, DOI: 10.5772/26165.

Ines Čeh, Sandi Pohorec, Marjan Mernik and Milan Zorman (August 12th 2010). Robot Learning of Domain Specific Knowledge from Natural Language Sources, Robot Learning, Suraiya Jabin, IntechOpen, DOI: 10.5772/10249. 

Igor Sheremet (November 21st 2018). Multiset-Based Knowledge Representation for the Assessment and Optimization of Large-Scale Sociotechnical Systems, Enhanced Expert Systems, Petrică Vizureanu, IntechOpen, DOI: 10.5772/intechopen.81698.

Representing unknown and missing knowledge about the environment is fundamental to leverage robot behavior and improve its performance in completing a task. However, reconstructing spatial knowledge beyond the sensory horizon of the robot is an extremely
challenging task. Existing approaches assume that the environment static and features repetitive patterns (e.g. rectangular rooms) or that it can be all generalized with pre-trained models. Our goal is to remove such assumptions and to introduce a novel methodology that allows the robot to represent unknown spatial knowledge in dynamic and unstructured environments. To this end, we exploit generative learning to (1) learn a distribution of spatial landmarks observed during the robot mission and to (2) generate missing in- formation in real-time. The proposed approach aims at supporting planning and decision-making processes needed for robot behaviors. In this paper, we describe architecture modeling the proposed approach and a first validation on a mobile platform.

AAMAS 2020

GUESs: Generative Modeling of Unknown Environments and Spatial Abstraction for robots

generative learning

robot learning

knowledge representation

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

IEEE RO-MAN 2021

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

In participatory budgeting, citizens can take part in the decision on which projects a city should spend money. Formally, the input is a set of items, each having a certain cost, while agents can express their preferences. The task is to choose a set of items respecting a given bound. Recently Talmon and Faliszewski introduced a framework for budgeting based on approval votes. This paper revisits the introduced methods axiomatically from an irresolute point of view, especially showing that two of the proposed methods coincide. The study is complemented by approximation results.


Irresolute Approval-based Budgeting

When an AI system interacts with multiple users, it frequently needs to make allocation decisions. For instance, a virtual agent decides whom to pay attention to in a group setting, or a factory robot selects a worker to deliver a part. Demonstrating fairness in decision making is essential for such systems to be broadly accepted. We introduce a Multi-Armed Bandit algorithm with fairness constraints, where fairness is defined as a minimum rate that a task or a resource is assigned to a user. The proposed algorithm uses contextual information about the users and the task and makes no assumptions on how the losses capturing the performance of different users are generated. We provide theoretical guarantees of performance and an online user study. We view this as an exciting step towards including fairness constraints in resource allocation decisions.


The Fair Contextual Multi-Armed Bandit

Reinforcement learning under partial observability poses two types of conceptually orthogonal learning challenges; learning a useful representation
of the observable past, and selecting actions based on that representation. While the latter is common to both fully and partially observable control
problems, the former is idiomatic to the partially observable case. Traditional reinforcement learning relies exclusively on the reward signal to train the
representation model, but rewards constitute a weak learning signal in partially observable settings. To help train richer representations, we discuss
and propose an auxiliary self-supervised learning task designed to help the primary reinforcement learning task. We further propose two training schemes
for training with auxiliary tasks, one inspired by the idea of promoting complementary representations. Empirical evaluations show that agents which
exploit auxiliary tasks and complementary representations learn better policies, and converge faster, compared to agents which use common reactive and
recurrent representations trained on the rewards alone.

Learning Complementary Representations of the Past using Auxiliary Tasks in Partially Observable Reinforcement Learning

We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate the proposed approaches in the setting of cooperative object manipulation with two simulated robot arms. Although we focus on a small system of two agents in this paper, both DA-RL and GT-RL apply to general multi-agent systems, and are expected to scale well to large systems.

Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation

Auction mechanism generally requires a trusted-third party as the market mediator to coordinate bidding and resource allocation via collecting private data from the agents, which may arouse severe privacy concerns and high computation overheads. To address such issues, we propose a novel privacy-aware double auction framework (namely PANDA) by designing an efficient cryptographic protocol to privately execute double auction for divisible resources among all the agents. To ensure privacy and truthfulness, PANDA delicately co-designs VCG auction and cryptographic protocol, which is equivalent to a mediator for sealed-bid auction of divisible resources. 


PANDA: Privacy-Aware Double Auction for Divisible Resources without a Mediator

Autonomous driving promises to transform road transport. Multi-vehicle and multi-lane scenarios, however, present unique challenges due to constrained navigation and unpredictable vehicle interactions. Learning-based methods—such as deep reinforcement learning—are emerging as a promising approach to automatically design intelligent driving policies that can cope with these challenges. Yet, the process of safely learning multi-vehicle driving behaviours is hard: while collisions—and their near-avoidance—are essential to the learning process, directly executing immature policies on autonomous vehicles raises considerable safety concerns. In this article, we present a safe and efficient framework that enables the learning of driving policies for autonomous vehicles operating in a shared workspace, where the absence of collisions cannot be guaranteed. Key to our learning procedure is a sim2real approach that uses real-world online policy adaptation in a mixed reality setup, where other vehicles and static obstacles exist in the virtual domain. This allows us to perform safe learning by simulating (and learning from) collisions between the learning agent(s) and other objects in virtual reality. Our results demonstrate that, after only a few runs in mixed reality, collisions are significantly reduced.


Mixed Reality Reinforcement Learning For Autonomous Multi-Lane Driving

An Agent-Based Model for Trajectory Modelling in Shared Spaces: A Combination of Expert Based and Deep Learning Approaches

Online social networks lack support for the collaborative management of access control. This is crucial for content that may involve multiple users such as photos, as this lack of support causes conflicts that lead to privacy violations. Previous research proposed collaborative mechanisms to support users in these cases, but most of these attempts fail to satisfy some desirable requirements, such as explainability, role-agnosticism, adaptability, and being utility- and value-driven at the same time. In this paper, we outline an agent architecture that has been designed to meet all these requirements.


Towards a Value-driven Explainable Agent for Collective Privacy

The booming sharing economy has a common challenge in motivating agents, by truthful actions, to follow the economic design of the market maker. For the well known BitTorrent network, there has been an array of theoretical research in the study of the truthfulness of the proportional response protocol formulated to model its tit-for-tat strategy. Some works confirm the truthfulness for deviations against the false name attack, weight cheating, and edge deleting. Others reveal it not truthful against Sybil attack where an agent may split its resource among its different copies. Though truthfulness cannot be guaranteed, the incentives are shown to generate a limited gain for agents pursuing such an attack in several special networks, such as trees, cliques, and cycles. In this work, we establish the first such results for general networks, proving a \theta(1) incentive ratio for any agent playing such an attack.


Limiting the Deviation Incentives in Resource Sharing Networks

Adversarial attacks have exposed a significant security vulnerability in state-of-the-art machine learning models. Among these models include deep reinforcement learning agents. The existing methods for attacking reinforcement learning agents assume the adversary either has access to the target agent's learned parameters or the environment that the agent interacts with. In this work, we propose a new class of threat models, called snooping threat models, that are unique to reinforcement learning. In these snooping threat models, the adversary does not have the ability to interact with the target agent's environment, and can only eavesdrop on the action and reward signals being exchanged between agent and environment. We show that adversaries operating in these highly constrained threat models can still launch devastating attacks against the target agent by training proxy models on related tasks and leveraging the transferability of adversarial examples.


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Irresolute Approval-based Budgeting

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Irresolute Approval-based Budgeting

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