Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Reinforcement Learning (RL) algorithms can in principle acquire complex robotic skills by learning from large amounts of data in the real world, collected via trial and error. However, most RL algorithms use a carefully engineered setup in order to collect data, requiring human supervision and intervention to provide episodic resets. This is particularly evident in challenging robotics problems, such as dexterous manipulation. To make data collection scalable, such applications require reset-free algorithms that are able to learn autonomously, without explicit instrumentation or human intervention. Most prior work in this area handles single-task learning. However, we might also want robots that can perform large repertoires of skills. At first, this would appear to only make the problem harder. However, the key observation we make in this work is that an appropriately chosen multi-task RL setting actually alleviates the reset-free learning challenge, with minimal additional machinery required. In effect, solving a multi-task problem can directly solve the reset-free problem since different combinations of tasks can serve to perform resets for other tasks. By learning multiple tasks together and appropriately sequencing them, we can effectively learn all of the tasks together reset-free. This type of multi-task learning can effectively scale reset-free learning schemes to much more complex problems, as we demonstrate in our experiments. We propose a simple scheme for multi-task learning that tackles the reset-free learning problem, and show its effectiveness at learning to solve complex dexterous manipulation tasks in both hardware and simulation without any explicit resets. This work shows the ability to learn in-hand manipulation behaviors in the real world with RL without any human intervention.

Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention

Continuum manipulators are deformable, passively compliant, and apt for manipulation in cluttered space. This paper addresses how to enable an $n$ -section continuum manipulator to probe an object, while gradually forming a whole-arm grasp in a cluttered environment. This approach is effective and efficient as evident from simulation and real experiments.

Progressive Planning of Continuum Grasping in Cluttered Space

Grasp configuration planning for a low-cost and easy-operation underactuated three-fingered robot hand

Universal Access in Human-Computer Interaction. Ambient Interaction

Although grasping of everyday objects has been a research topic over the last decades, it still is a crucial task for service robots. Several methods have been proposed to generate suitable grasps for objects. Many of them are restricted to a certain type of grasp or limited to a fixed number of contacts. In this paper we propose an algorithm based on reinforcement learning, to enable a service robot to grasp every kind of object with as many contacts as needed. The proposed method will be evaluated using a simulation with a three-fingered robotic hand.

Learning to grasp everyday objects using reinforcement-learning with automatic value cut-off

This paper presents an image based three dimensional reconstruction system for service-robot applications in case of daily table scenarios. Image driven environment perception is one of the main research topics in the field of autonomous robot applications and fundamental for further action-plannings like three dimensional collision detection and prevention for grasping tasks. Perception will be done at two spatial-temporal varying positions by a micro-head camera mounted on a six-degree-of-freedom robot-arm of our mobile service-robot TASER. The epipolar geometry and fundamentalmatrix will be computed by preliminary extracted corners of both input images detected by a Harris-cornerdetector. The input images will be rectified using the fundamentalmatrix to align corresponding scanlines together on the same vertical image coordinates. Afterwards a stereo correspondence is accomplished by a fast Birchfield algorithm that provides a 2.5 dimensional depth map of the scene. Based on the depth map a three dimensional textured point-cloud is represented as interactive OpenGL scene model for further actionplanning algorithms in three dimensional space.

Three-dimenisonal monocular scene reconstruction for service-robots - an application.

/pdf/three-dimenisonal-monocular-scene-reconstruction-for-service-1qmrr0p6z7.pdf

THREE-DIMENISONAL MONOCULAR SCENE RECONSTRUCTION FOR SERVICE-ROBOTS - An Application

This paper presents a novel approach to natural demonstration of manipulation skills for multimodal interactive robots. The main focus is on the natural demonstration of manipulation skills, especially grasping skills. In order to teach grasping skills to a multimodal interactive robot, a human instructor makes use of natural spoken language and grasping actions demonstrated to the robot. The proposed approach emphasizes on four different aspects of learning by demonstration: First, the dialog system for processing natural speech is considered. Second, an object detection and classification scheme for the robot is shown. Third, the correspondence problem is addressed by an algorithm for visual tracking of the demonstrator's hands in real time and the transformation of the tracking results into an approach trajectory for a robotic arm. The fourth aspect addresses the fine-tuning of the robot's hand configuration for each grasp. It introduces a criterion to evaluate a grasp for stability and possible reuse of a grasped object. The approach produces stable grasps and is applied and evaluated on a multimodal service robot.

/pdf/natural-demonstration-of-manipulation-skills-for-multimodal-yb5wnr6t78.pdf

Natural demonstration of manipulation skills for multimodal interactive robots

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Tim Baier-Löwenstein

Author Tools

Chat about Author

Papers

Learning to grasp everyday objects using reinforcement-learning with automatic value cut-off

Three-dimenisonal monocular scene reconstruction for service-robots - an application.

THREE-DIMENISONAL MONOCULAR SCENE RECONSTRUCTION FOR SERVICE-ROBOTS - An Application

Natural demonstration of manipulation skills for multimodal interactive robots