31,573 research outputs found
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself are crucial. In this work, we propose a novel framework for
probabilistic online motion planning with online adaptation based on a
bio-inspired stochastic recurrent neural network. By using learning signals
which mimic the intrinsic motivation signalcognitive dissonance in addition
with a mental replay strategy to intensify experiences, the stochastic
recurrent network can learn from few physical interactions and adapts to novel
environments in seconds. We evaluate our online planning and adaptation
framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
shown by learning unknown workspace constraints sample-efficiently from few
physical interactions while following given way points.Comment: accepted in Neural Network
Towards adaptive multi-robot systems: self-organization and self-adaptation
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible
When the path is never shortest: a reality check on shortest path biocomputation
Shortest path problems are a touchstone for evaluating the computing
performance and functional range of novel computing substrates. Much has been
published in recent years regarding the use of biocomputers to solve minimal
path problems such as route optimisation and labyrinth navigation, but their
outputs are typically difficult to reproduce and somewhat abstract in nature,
suggesting that both experimental design and analysis in the field require
standardising. This chapter details laboratory experimental data which probe
the path finding process in two single-celled protistic model organisms,
Physarum polycephalum and Paramecium caudatum, comprising a shortest path
problem and labyrinth navigation, respectively. The results presented
illustrate several of the key difficulties that are encountered in categorising
biological behaviours in the language of computing, including biological
variability, non-halting operations and adverse reactions to experimental
stimuli. It is concluded that neither organism examined are able to efficiently
or reproducibly solve shortest path problems in the specific experimental
conditions that were tested. Data presented are contextualised with biological
theory and design principles for maximising the usefulness of experimental
biocomputer prototypes.Comment: To appear in: Adamatzky, A (Ed.) Shortest path solvers. From software
to wetware. Springer, 201
Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges
Intelligent escape is an interdisciplinary field that employs artificial
intelligence (AI) techniques to enable robots with the capacity to
intelligently react to potential dangers in dynamic, intricate, and
unpredictable scenarios. As the emphasis on safety becomes increasingly
paramount and advancements in robotic technologies continue to advance, a wide
range of intelligent escape methodologies has been developed in recent years.
This paper presents a comprehensive survey of state-of-the-art research work on
intelligent escape of robotic systems. Four main methods of intelligent escape
are reviewed, including planning-based methodologies, partitioning-based
methodologies, learning-based methodologies, and bio-inspired methodologies.
The strengths and limitations of existing methods are summarized. In addition,
potential applications of intelligent escape are discussed in various domains,
such as search and rescue, evacuation, military security, and healthcare. In an
effort to develop new approaches to intelligent escape, this survey identifies
current research challenges and provides insights into future research trends
in intelligent escape.Comment: This paper is accepted by Journal of Intelligent and Robotic System
Urban Swarms: A new approach for autonomous waste management
Modern cities are growing ecosystems that face new challenges due to the
increasing population demands. One of the many problems they face nowadays is
waste management, which has become a pressing issue requiring new solutions.
Swarm robotics systems have been attracting an increasing amount of attention
in the past years and they are expected to become one of the main driving
factors for innovation in the field of robotics. The research presented in this
paper explores the feasibility of a swarm robotics system in an urban
environment. By using bio-inspired foraging methods such as multi-place
foraging and stigmergy-based navigation, a swarm of robots is able to improve
the efficiency and autonomy of the urban waste management system in a realistic
scenario. To achieve this, a diverse set of simulation experiments was
conducted using real-world GIS data and implementing different garbage
collection scenarios driven by robot swarms. Results presented in this research
show that the proposed system outperforms current approaches. Moreover, results
not only show the efficiency of our solution, but also give insights about how
to design and customize these systems.Comment: Manuscript accepted for publication in IEEE ICRA 201
- …