2,139 research outputs found
Sequential decision making in artificial musical intelligence
Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science
Collaborative autonomy in heterogeneous multi-robot systems
As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition.
This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems.
Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots
Spatial representation for planning and executing robot behaviors in complex environments
Robots are already improving our well-being and productivity in
different applications such as industry, health-care and indoor
service applications. However, we are still far from developing (and
releasing) a fully functional robotic agent that can autonomously
survive in tasks that require human-level
cognitive capabilities. Robotic systems on the market, in fact, are
designed to address specific applications, and can only run
pre-defined behaviors to robustly repeat few tasks (e.g., assembling
objects parts, vacuum cleaning). They internal representation of the
world is usually constrained to the task they are performing, and
does not allows for generalization to other
scenarios. Unfortunately, such a paradigm only apply to a very
limited set of domains, where the environment can be assumed to be
static, and its dynamics can be handled before
deployment. Additionally, robots configured in this way will
eventually fail if their "handcrafted'' representation of the
environment does not match the external world.
Hence, to enable more sophisticated cognitive skills, we investigate
how to design robots to properly represent the environment and
behave accordingly. To this end, we formalize a representation of
the environment that enhances the robot spatial knowledge to
explicitly include a representation of its own actions. Spatial
knowledge constitutes the core of the robot understanding of the
environment, however it is not sufficient to represent what the
robot is capable to do in it. To overcome such a limitation, we
formalize SK4R, a spatial knowledge representation for robots which
enhances spatial knowledge with a novel and "functional"
point of view that explicitly models robot actions. To this end, we
exploit the concept of affordances, introduced to express
opportunities (actions) that objects offer to an agent. To encode
affordances within SK4R, we define the "affordance
semantics" of actions that is used to annotate an environment, and
to represent to which extent robot actions support goal-oriented
behaviors.
We demonstrate the benefits of a functional representation of the
environment in multiple robotic scenarios that traverse and
contribute different research topics relating to: robot knowledge
representations, social robotics, multi-robot systems and robot
learning and planning. We show how a domain-specific representation,
that explicitly encodes affordance semantics, provides the robot
with a more concrete understanding of the environment and of the
effects that its actions have on it. The goal of our work is to
design an agent that will no longer execute an action, because of
mere pre-defined routine, rather, it will execute an actions because
it "knows'' that the resulting state leads one step closer to
success in its task
Meta-Referential Games to Learn Compositional Learning Behaviours
Human beings use compositionality to generalise from past experiences to
novel experiences. We assume a separation of our experiences into fundamental
atomic components that can be recombined in novel ways to support our ability
to engage with novel experiences. We frame this as the ability to learn to
generalise compositionally, and we will refer to behaviours making use of this
ability as compositional learning behaviours (CLBs). A central problem to
learning CLBs is the resolution of a binding problem (BP). While it is another
feat of intelligence that human beings perform with ease, it is not the case
for state-of-the-art artificial agents. Thus, in order to build artificial
agents able to collaborate with human beings, we propose to develop a novel
benchmark to investigate agents' abilities to exhibit CLBs by solving a
domain-agnostic version of the BP. We take inspiration from the language
emergence and grounding framework of referential games and propose a
meta-learning extension of referential games, entitled Meta-Referential Games,
and use this framework to build our benchmark, that we name Symbolic Behaviour
Benchmark (S2B). We provide baseline results showing that our benchmark is a
compelling challenge that we hope will spur the research community towards
developing more capable artificial agents.Comment: work in progres
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration
We consider the problem of cooperative exploration where multiple robots need
to cooperatively explore an unknown region as fast as possible. Multi-agent
reinforcement learning (MARL) has recently become a trending paradigm for
solving this challenge. However, existing MARL-based methods adopt
action-making steps as the metric for exploration efficiency by assuming all
the agents are acting in a fully synchronous manner: i.e., every single agent
produces an action simultaneously and every single action is executed
instantaneously at each time step. Despite its mathematical simplicity, such a
synchronous MARL formulation can be problematic for real-world robotic
applications. It can be typical that different robots may take slightly
different wall-clock times to accomplish an atomic action or even periodically
get lost due to hardware issues. Simply waiting for every robot being ready for
the next action can be particularly time-inefficient. Therefore, we propose an
asynchronous MARL solution, Asynchronous Coordination Explorer (ACE), to tackle
this real-world challenge. We first extend a classical MARL algorithm,
multi-agent PPO (MAPPO), to the asynchronous setting and additionally apply
action-delay randomization to enforce the learned policy to generalize better
to varying action delays in the real world. Moreover, each navigation agent is
represented as a team-size-invariant CNN-based policy, which greatly benefits
real-robot deployment by handling possible robot lost and allows
bandwidth-efficient intra-agent communication through low-dimensional CNN
features. We first validate our approach in a grid-based scenario. Both
simulation and real-robot results show that ACE reduces over 10% actual
exploration time compared with classical approaches. We also apply our
framework to a high-fidelity visual-based environment, Habitat, achieving 28%
improvement in exploration efficiency.Comment: This paper is accepted by AAMAS 2023. The source code can be found in
https://github.com/yang-xy20/async_mapp
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