283 research outputs found
Multiform Adaptive Robot Skill Learning from Humans
Object manipulation is a basic element in everyday human lives. Robotic
manipulation has progressed from maneuvering single-rigid-body objects with
firm grasping to maneuvering soft objects and handling contact-rich actions.
Meanwhile, technologies such as robot learning from demonstration have enabled
humans to intuitively train robots. This paper discusses a new level of robotic
learning-based manipulation. In contrast to the single form of learning from
demonstration, we propose a multiform learning approach that integrates
additional forms of skill acquisition, including adaptive learning from
definition and evaluation. Moreover, going beyond state-of-the-art technologies
of handling purely rigid or soft objects in a pseudo-static manner, our work
allows robots to learn to handle partly rigid partly soft objects with
time-critical skills and sophisticated contact control. Such capability of
robotic manipulation offers a variety of new possibilities in human-robot
interaction.Comment: Accepted to 2017 Dynamic Systems and Control Conference (DSCC),
Tysons Corner, VA, October 11-1
Robot Composite Learning and the Nunchaku Flipping Challenge
Advanced motor skills are essential for robots to physically coexist with
humans. Much research on robot dynamics and control has achieved success on
hyper robot motor capabilities, but mostly through heavily case-specific
engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous
manner, robot learning from human demonstration (LfD) has achieved great
progress, but still has limitations handling dynamic skills and compound
actions. In this paper, we present a composite learning scheme which goes
beyond LfD and integrates robot learning from human definition, demonstration,
and evaluation. The method tackles advanced motor skills that require dynamic
time-critical maneuver, complex contact control, and handling partly soft
partly rigid objects. We also introduce the "nunchaku flipping challenge", an
extreme test that puts hard requirements to all these three aspects. Continued
from our previous presentations, this paper introduces the latest update of the
composite learning scheme and the physical success of the nunchaku flipping
challenge
A Review of Motion Planning Algorithms for Robotic Arm Systems
Motion planning plays a vital role in the field of robotics. This paper discusses the latest advancements made in the research and development of various algorithms and approaches in motion planning in the past five years, with a strong focus on robotic arm systems. Most of the recent motion planning algorithms are based on random sampling algorithms. More effective algorithms such as optimization-based, Probabilistic Movement Primitives (ProMPs)-based and physics-based methods are feasible research directions to explore to improve the effectiveness. The evaluation benchmarking of the algorithm is a worthy research direction. The model-based methods can improve the efficiency of the task, but it has less ability to deal with accidents. In contrast, model-free methods can solve this problem, but it takes a long time to compute. This paper also provides an insight into the robotic manipulation of rigid and non-rigid (deformable) objects is explored. Based on the study, some challenges and future research trends are summarized, and some algorithms and approaches are suggested for the most efficient use of the robotic arms
System-Engineered Miniaturized Robots: From Structure to Intelligence
The development of small machines, once envisioned by Feynman decades ago, has stimulated significant research in materials science, robotics, and computer science. Over the past years, the field of miniaturized robotics has rapidly expanded with many research groups contributing to the numerous challenges inherent to this field. Smart materials have played a particularly important role as they have imparted miniaturized robots with new functionalities and distinct capabilities. However, despite all efforts and many available soft materials and innovative technologies, a fully autonomous system-engineered miniaturized robot (SEMR) of any practical relevance has not been developed yet. In this review, the foundation of SEMRs is discussed and six main areas (structure, motion, sensing, actuation, energy, and intelligence) which require particular efforts to push the frontiers of SEMRs further are identified. During the past decade, miniaturized robotic research has mainly relied on simplicity in design, and fabrication. A careful examination of current SEMRs that are physically, mechanically, and electrically engineered shows that they fall short in many ways concerning miniaturization, full-scale integration, and self-sufficiency. Some of these issues have been identified in this review. Some are inevitably yet to be explored, thus, allowing to set the stage for the next generation of intelligent, and autonomously operating SEMRs
Automated Sensing Methods in Soft Stretchable Sensors for Soft Robotic Gripper
A soft robot is made from deformable and flexible materials such as silicone, rubber, polymers, etc. Soft robotics is a rapidly evolving field where the human-robot-interaction and bio-inspired design align. The physical characteristics such as highly deformable material and dexterity make soft robots widely applicable. A soft robotic gripper is a robotic hand that acts like a human hand and grasps any object. The most common applications of soft robotics grippers are gripping and locomotion in sensitive applications where high dynamic and sensitivity are essential. Nowadays, soft robotics grippers are used without any sensing method and feedback as it is crucial to make the output feedback from the gripper. The major drawback of soft robotic grippers is their need for more precision sensing. In traditional robots, we can integrate any sensor to detect the force and orientation of objects. Still, soft robotic grippers rely on the deformation sensing method, where the sensor must be highly flexible and deformable. With a precise sensing method, it is easier to determine the exact position or orientation of the object being gripped, and it limits the application of the soft robotic gripper. Sometimes, soft robots are employed in harsh environments to solve problems. With the sensing feedback, automation may become more reliable and succeed altogether. So, in this research, we have designed and fabricated a soft sensor to integrate with the gripper and to observe the feedback of the gripper. We propose integrated multimodal sensing that incorporates applied pressure and resistance change. The sensor provides feedback when the grippers hold any object, and the output response is the resistance change of the sensor. The liquid metal is susceptible and can respond to low force levels. We presented the 3D design, FEM simulation, fabrication, and integration of the gripper and sensor, and by showing both simulation and experimental results, the gripper is validated for real-time application. FEM simulation simulates behavior, optimizing design and predicting performance. We have designed and fabricated a soft sensor that yields microfluidic channel arrays embedded with liquid metal Galinstan alloy and a soft robotic gripper hand. Different printing processes and characterization results are presented for the sensor and actuator. The fabrication process of the gripper and sensor is adequately described. The gripper output characteristics are tested for bending angle, load test, elongation, and object holding under various applied pressure. Additionally, the sensor was tested for stretchability, linearity and durability, and human gesture integration with the finger, and this sensor can be easily reused/ reproduced. Furthermore, the sensor exhibits good sensitivity concerning different pressure and grasping various objects. Finally, we collected data using this sensor-integrated gripper and trained the dataset using machine learning models for automation. With more data, this can be an autonomous gripper with intelligent sensing methodologies. Moreover, this proposed stretchable sensor can be integrated into any existing gripper for innovative real-time applications
NASA Tech Briefs, July 1992
Topics include: New Product Ideas; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences
On microelectronic self-learning cognitive chip systems
After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory.
From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research.
And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting
conscious phenomena should crucially be restricted to extremely well defined constraints.
Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details.
In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche
Selected papers on Hands-on Science II
This second volume of the "Selected Papers on Hands-on Science" the Hands-on Science Network is publishing, reunites some of the most relevant works presented at the 2008, 2009, 2010 and 2011 editions of the annual International Conference on Hands-on Science. From pre-school science education to lifelong science learning and teacher training, in formal non-formal and informal contexts, the large diversified range of works that conforms this book surely renders it an important tool to schools and educators and all involved in science education and on the promotion of scientific literacy.info:eu-repo/semantics/publishedVersio
The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies
This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed
- …