313 research outputs found
A Framework for Interactive Teaching of Virtual Borders to Mobile Robots
The increasing number of robots in home environments leads to an emerging
coexistence between humans and robots. Robots undertake common tasks and
support the residents in their everyday life. People appreciate the presence of
robots in their environment as long as they keep the control over them. One
important aspect is the control of a robot's workspace. Therefore, we introduce
virtual borders to precisely and flexibly define the workspace of mobile
robots. First, we propose a novel framework that allows a person to
interactively restrict a mobile robot's workspace. To show the validity of this
framework, a concrete implementation based on visual markers is implemented.
Afterwards, the mobile robot is capable of performing its tasks while
respecting the new virtual borders. The approach is accurate, flexible and less
time consuming than explicit robot programming. Hence, even non-experts are
able to teach virtual borders to their robots which is especially interesting
in domains like vacuuming or service robots in home environments.Comment: 7 pages, 6 figure
A novel plasticity rule can explain the development of sensorimotor intelligence
Grounding autonomous behavior in the nervous system is a fundamental
challenge for neuroscience. In particular, the self-organized behavioral
development provides more questions than answers. Are there special functional
units for curiosity, motivation, and creativity? This paper argues that these
features can be grounded in synaptic plasticity itself, without requiring any
higher level constructs. We propose differential extrinsic plasticity (DEP) as
a new synaptic rule for self-learning systems and apply it to a number of
complex robotic systems as a test case. Without specifying any purpose or goal,
seemingly purposeful and adaptive behavior is developed, displaying a certain
level of sensorimotor intelligence. These surprising results require no system
specific modifications of the DEP rule but arise rather from the underlying
mechanism of spontaneous symmetry breaking due to the tight
brain-body-environment coupling. The new synaptic rule is biologically
plausible and it would be an interesting target for a neurobiolocal
investigation. We also argue that this neuronal mechanism may have been a
catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video
A psychology based approach for longitudinal development in cognitive robotics.
A major challenge in robotics is the ability to learn, from novel experiences, new behavior that is useful for achieving new goals and skills. Autonomous systems must be able to learn solely through the environment, thus ruling out a priori task knowledge, tuning, extensive training, or other forms of pre-programming. Learning must also be cumulative and incremental, as complex skills are built on top of primitive skills. Additionally, it must be driven by intrinsic motivation because formative experience is gained through autonomous activity, even in the absence of extrinsic goals or tasks. This paper presents an approach to these issues through robotic implementations inspired by the learning behavior of human infants. We describe an approach to developmental learning and present results from a demonstration of longitudinal development on an iCub humanoid robot. The results cover the rapid emergence of staged behavior, the role of constraints in development, the effect of bootstrapping between stages, and the use of a schema memory of experiential fragments in learning new skills. The context is a longitudinal experiment in which the robot advanced from uncontrolled motor babbling to skilled hand/eye integrated reaching and basic manipulation of objects. This approach offers promise for further fast and effective sensory-motor learning techniques for robotic learning
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
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Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search
In principle, reinforcement learning and policy search methods can enable
robots to learn highly complex and general skills that may allow them to
function amid the complexity and diversity of the real world. However, training
a policy that generalizes well across a wide range of real-world conditions
requires far greater quantity and diversity of experience than is practical to
collect with a single robot. Fortunately, it is possible for multiple robots to
share their experience with one another, and thereby, learn a policy
collectively. In this work, we explore distributed and asynchronous policy
learning as a means to achieve generalization and improved training times on
challenging, real-world manipulation tasks. We propose a distributed and
asynchronous version of Guided Policy Search and use it to demonstrate
collective policy learning on a vision-based door opening task using four
robots. We show that it achieves better generalization, utilization, and
training times than the single robot alternative.Comment: Submitted to the IEEE International Conference on Robotics and
Automation 201
A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots
Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions
From Rolling Over to Walking: Enabling Humanoid Robots to Develop Complex Motor Skills
This paper presents an innovative method for humanoid robots to acquire a
comprehensive set of motor skills through reinforcement learning. The approach
utilizes an achievement-triggered multi-path reward function rooted in
developmental robotics principles, facilitating the robot to learn gross motor
skills typically mastered by human infants within a single training phase. The
proposed method outperforms standard reinforcement learning techniques in
success rates and learning speed within a simulation environment. By leveraging
the principles of self-discovery and exploration integral to infant learning,
this method holds the potential to significantly advance humanoid robot motor
skill acquisition.Comment: 8 pages, 9 figures. Submitted to IEEE Robotics and Automation
Letters. Video available at https://youtu.be/d0RqrW1Ezj
Developmental Bootstrapping of AIs
Although some current AIs surpass human abilities in closed artificial worlds
such as board games, their abilities in the real world are limited. They make
strange mistakes and do not notice them. They cannot be instructed easily, fail
to use common sense, and lack curiosity. They do not make good collaborators.
Mainstream approaches for creating AIs are the traditional manually-constructed
symbolic AI approach and generative and deep learning AI approaches including
large language models (LLMs). These systems are not well suited for creating
robust and trustworthy AIs. Although it is outside of the mainstream, the
developmental bootstrapping approach has more potential. In developmental
bootstrapping, AIs develop competences like human children do. They start with
innate competences. They interact with the environment and learn from their
interactions. They incrementally extend their innate competences with
self-developed competences. They interact and learn from people and establish
perceptual, cognitive, and common grounding. They acquire the competences they
need through bootstrapping. However, developmental robotics has not yet
produced AIs with robust adult-level competences. Projects have typically
stopped at the Toddler Barrier corresponding to human infant development at
about two years of age, before their speech is fluent. They also do not bridge
the Reading Barrier, to skillfully and skeptically draw on the socially
developed information resources that power current LLMs. The next competences
in human cognitive development involve intrinsic motivation, imitation
learning, imagination, coordination, and communication. This position paper
lays out the logic, prospects, gaps, and challenges for extending the practice
of developmental bootstrapping to acquire further competences and create
robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure
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