1,511 research outputs found
Towards Active Event Recognition
Directing robot attention to recognise activities and to anticipate events like goal-directed actions is a crucial skill for human-robot interaction. Unfortunately, issues like intrinsic time constraints, the spatially distributed nature of the entailed information sources, and the existence of a multitude of unobservable states affecting the system, like latent intentions, have long rendered achievement of such skills a rather elusive goal. The problem tests the limits of current attention control systems. It requires an integrated solution for tracking, exploration and recognition, which traditionally have been seen as separate problems in active vision.We propose a probabilistic generative framework based on a mixture of Kalman filters and information gain maximisation that uses predictions in both recognition and attention-control. This framework can efficiently use the observations of one element in a dynamic environment to provide information on other elements, and consequently enables guided exploration.Interestingly, the sensors-control policy, directly derived from first principles, represents the intuitive trade-off between finding the most discriminative clues and maintaining overall awareness.Experiments on a simulated humanoid robot observing a human executing goal-oriented actions demonstrated improvement on recognition time and precision over baseline systems
Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection under Ego-motion
In recent years, dynamic vision sensors (DVS), also known as event-based
cameras or neuromorphic sensors, have seen increased use due to various
advantages over conventional frame-based cameras. Using principles inspired by
the retina, its high temporal resolution overcomes motion blurring, its high
dynamic range overcomes extreme illumination conditions and its low power
consumption makes it ideal for embedded systems on platforms such as drones and
self-driving cars. However, event-based data sets are scarce and labels are
even rarer for tasks such as object detection. We transferred discriminative
knowledge from a state-of-the-art frame-based convolutional neural network
(CNN) to the event-based modality via intermediate pseudo-labels, which are
used as targets for supervised learning. We show, for the first time,
event-based car detection under ego-motion in a real environment at 100 frames
per second with a test average precision of 40.3% relative to our annotated
ground truth. The event-based car detector handles motion blur and poor
illumination conditions despite not explicitly trained to do so, and even
complements frame-based CNN detectors, suggesting that it has learnt
generalized visual representations
Evolution of Adaptive Behaviour in Robots by Means of Darwinian Selection
ii.34., humans have been intrigued by the origin and mechanisms underlying complexity in nature. Darwin suggested that adaptation and complexity could evolve by natural selection acting successively on numerous small, heritable modifications. But is this enough? Here, we describe selected studies of experimental evolution with robots to illustrate how the process of natural selection can lead to the evolution of complex traits such as adaptive behaviours. Just a few hundred generations of selection are sufficient to allow robots to evolve collision-free movement, homing, sophisticate
Coordinated Multi-Agent Imitation Learning
We study the problem of imitation learning from demonstrations of multiple
coordinating agents. One key challenge in this setting is that learning a good
model of coordination can be difficult, since coordination is often implicit in
the demonstrations and must be inferred as a latent variable. We propose a
joint approach that simultaneously learns a latent coordination model along
with the individual policies. In particular, our method integrates unsupervised
structure learning with conventional imitation learning. We illustrate the
power of our approach on a difficult problem of learning multiple policies for
fine-grained behavior modeling in team sports, where different players occupy
different roles in the coordinated team strategy. We show that having a
coordination model to infer the roles of players yields substantially improved
imitation loss compared to conventional baselines.Comment: International Conference on Machine Learning 201
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Implementation of multi-algorithm controllers for path determination in mobile robot systems
textRecent advancements in control systems, such as the ones used in missile technology in the military or autonomous vehicle development have motivated this study in an attempt to explore various control algorithms and their implementation relevant those applications. Both missile interceptor and autonomous vehicle technology require precise and responsive control system to accurately determine the projectile path of pursuer to strike a moving target or reach a static finish line.The objective of this study is to investigate the performance of several control techniques for a mobile robot to autonomously track and pursue a moving object. Computer model is developed to numerically predict the path taken by the pursuer as it tracks an object moving in regular or random manner. In the computer simulation, the robot's path is calculated using three different techniques: reactive controller, linear estimation, and artificial neural network. Fitness of each method may be determined by evaluating the controller against several factors, such as interception time, steady-state positional error, steady-state time (settling time) and algorithm complexity, listed in decreasing order of importance. A working experimental model is developed to validate the controller selection determined from the computer model simulation. In the experimental setting, the primary inputs to the robot are visual images from cameras. The experiments are carried out with the robot receiving visual inputs from two different perspectives, overhead and frontal vision. Robust image processing technique becomes a topic of significant importance for the system. To manipulate visual images in real-time from raw inputs to comprehensible data, while maintaining fast computational time is a challenge that is addressed in this study. The results from computer simulations show that artificial neural network is a more powerful control algorithm, capable of estimating the object's path more accurately than the other two controllers, resulting in smaller steady-state positional error. The experimental results confirm this conclusion as artificial neural network outperforms the reactive and linear controller by intercepting the object more quickly, i.e. shorter interception time.Mechanical Engineerin
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
Modeling of real-world biological multi-agents is a fundamental problem in
various scientific and engineering fields. Reinforcement learning (RL) is a
powerful framework to generate flexible and diverse behaviors in cyberspace;
however, when modeling real-world biological multi-agents, there is a domain
gap between behaviors in the source (i.e., real-world data) and the target
(i.e., cyberspace for RL), and the source environment parameters are usually
unknown. In this paper, we propose a method for adaptive action supervision in
RL from real-world demonstrations in multi-agent scenarios. We adopt an
approach that combines RL and supervised learning by selecting actions of
demonstrations in RL based on the minimum distance of dynamic time warping for
utilizing the information of the unknown source dynamics. This approach can be
easily applied to many existing neural network architectures and provide us
with an RL model balanced between reproducibility as imitation and
generalization ability to obtain rewards in cyberspace. In the experiments,
using chase-and-escape and football tasks with the different dynamics between
the unknown source and target environments, we show that our approach achieved
a balance between the reproducibility and the generalization ability compared
with the baselines. In particular, we used the tracking data of professional
football players as expert demonstrations in football and show successful
performances despite the larger gap between behaviors in the source and target
environments than the chase-and-escape task.Comment: 14 pages, 5 figure
Achieving mouse-level strategic evasion performance using real-time computational planning
Planning is an extraordinary ability in which the brain imagines and then
enacts evaluated possible futures. Using traditional planning models, computer
scientists have attempted to replicate this capacity with some level of success
but ultimately face a reoccurring limitation: as the plan grows in steps, the
number of different possible futures makes it intractable to determine the
right sequence of actions to reach a goal state. Based on prior theoretical
work on how the ecology of an animal governs the value of spatial planning, we
developed a more efficient biologically-inspired planning algorithm, TLPPO.
This algorithm allows us to achieve mouselevel predator evasion performance
with orders of magnitude less computation than a widespread algorithm for
planning in the situations of partial observability that typify predator-prey
interactions. We compared the performance of a real-time agent using TLPPO
against the performance of live mice, all tasked with evading a robot predator.
We anticipate these results will be helpful to planning algorithm users and
developers, as well as to areas of neuroscience where robot-animal interaction
can provide a useful approach to studying the basis of complex behaviors.Comment: 6 pages, 4 figures, ICRA 202
Motion Planning
Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms
Entropy and Fractal Techniques for Monitoring Fish Behaviour and Welfare in Aquacultural Precision Fish Farming—A Review
In a non-linear system, such as a biological system, the change of the output (e.g., behaviour) is not proportional to the change of the input (e.g., exposure to stressors). In addition, biological systems also change over time, i.e., they are dynamic. Non-linear dynamical analyses of biological systems have revealed hidden structures and patterns of behaviour that are not discernible by classical methods. Entropy analyses can quantify their degree of predictability and the directionality of individual interactions, while fractal dimension (FD) analyses can expose patterns of behaviour within apparently random ones. The incorporation of these techniques into the architecture of precision fish farming (PFF) and intelligent aquaculture (IA) is becoming increasingly necessary to understand and predict the evolution of the status of farmed fish. This review summarizes recent works on the application of entropy and FD techniques to selected individual and collective fish behaviours influenced by the number of fish, tagging, pain, preying/feed search, fear/anxiety (and its modulation) and positive emotional contagion (the social contagion of positive emotions). Furthermore, it presents an investigation of collective and individual interactions in shoals, an exposure of the dynamics of inter-individual relationships and hierarchies, and the identification of individuals in groups. While most of the works have been carried out using model species, we believe that they have clear applications in PFF. The review ends by describing some of the major challenges in the field, two of which are, unsurprisingly, the acquisition of high-quality, reliable raw data and the construction of large, reliable databases of non-linear behavioural data for different species and farming conditions.The work was supported by the Spanish MINECO (Grant RTC-2014–2837-2- “SELATUN: Minimización de la problemática del mercurio del atún y valorización del atún como alimento saludable, Programa Retos-Colaboración 2014”. The funding source had no involvement in the preparation of this manuscript
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