25 papers · ranked by Valyu relevance
Sha Luo, Mingyue Zhang, Yongbo Zhuang, Cheng Ma + 1 more
Path planning is an essential part of robot intelligence. In this paper, we summarize the characteristics of path planning of industrial robots. And owing to the probabilistic completeness, we review the rapidly-exploring random tree (RRT) algorithm which is widely used in the path planning of industrial robots. Aiming…
Ignacio Fidalgo Astorquia, Guillermo Villate-Castillo, Alberto Tellaeche, Juan-Ignacio Vazquez
'Alberto Tellaeche' 'Juan-Ignacio Vazquez'] This study presents a comprehensive comparison between classical sampling-based motion planners from the Open Motion Planning Library (OMPL) and a learning-based planner based on Soft Actor-Critic (SAC) for motion planning in industrial robotic arms. Using a UR3e robot…
Daksh Dobhal, Jayesh Nagpal, Rushang Karia, Pulkit Verma + 3 more
'Rashmeet Kaur Nayyar' 'Naman Shah' 'Siddharth Srivastava'] Understanding how robots plan and execute tasks is crucial in today's world, where they are becoming more prevalent in our daily lives. However, teaching non-experts the complexities of robot planning can be challenging. This work presents an opensource…
Alessio Capitanelli, Fulvio Mastrogiovanni
Symbolic task planning is a widely used approach to enforce robot autonomy due to its ease of understanding and deployment in engineered robot architectures. However, techniques for symbolic task planning are difficult to scale in real-world, highly dynamic, human-robot collaboration scenarios because of the poor…
Weihang Guo, Theodoros Tyrovouzis, Emiliano Flores, Clayton W. Ramsey + 4 more
The Open Motion Planning Library (OMPL), first released in 2008, has become a cornerstone of the motion planning community, providing implementations of a wide range of state-of-the-art sampling-based algorithms. Over almost two decades of continuous development, we have steadily expanded the library with new planners…
Troy McMahon, Aravind Sivaramakrishnan, Edgar Granados, Kostas E. Bekris
'Kostas E. Bekris'] | 1 | | Introduction | | | | | | 268 | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 2 | | Sampling-based Motion Planning | | | | | | 273 | | | 3 | | Learning Primitives of | | | | | | | | | | | Sampling-based Motion Planning | | | | | | 278 | | | | 3.1 | Sampling Sequences | . |…
Yongping Pan, Basil M. Al-Hadithi, Chenguang Yang
Artificial intelligence (AI) is intelligence demonstrated by machines, in contrast to the natural intelligence demonstrated by humans. Examples of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects, which…
Daniele Meli, Hirenkumar Nakawala, Paolo Fiorini
Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines…
Raquel Fuentetaja, Angel García-Olaya, Javier García, José Carlos González + 1 more
'José Carlos González' 'Fernando Fernández'] Using Automated Planning for the high level control of robotic architectures is becoming very popular thanks mainly to its capability to define the tasks to perform in a declarative way. However, classical planning tasks, even in its basic standard Planning Domain Definition…
Claudio Zito
| 1 | Introduction 1 | | --- | --- | | | 1.1 Deriving Controllers for Embedded Systems 2 | | | 1.2 Motion Planning in Unstructured Environments 4 | | | 1.3 The Belief Space 5 | | | 1.4 Modeling Uncertainty with Markov Processes 6 | | | 1.5 Related Work 7 | | | 1.6 Our Approach 8 | | | 1.7 Working scenarios 8 | | | 1.8…
Helen Harman, Keshav Chintamani, Pieter Simoens
By coupling a robot to a smart environment, the robot can sense state beyond the perception range of its onboard sensors and gain greater actuation capabilities. Nevertheless, incorporating the states and actions of Internet of Things (IoT) devices into the robot’s onboard planner increases the computational load, and…
Ruikai Liu, Guangxi Wan, Maowei Jiang, Haojie Chen + 2 more
'Ming Xie'] The Agile Robotics for Industrial Automation Competition (ARIAC) was established to advance flexible manufacturing, aiming to increase the agility of robotic assembly systems in unstructured and dynamic industrial environments. ARIAC 2023 introduced eight agility challenges involving faulty parts, flipped…
Steve Macenski, Tom Moore, David V. Lu, Alexey Merzlyakov
— The Robot Operating System 2 (ROS 2) is rapidly impacting the intelligent machines sector - on space missions, large agriculture equipment, multi-robot fleets, and more. Its success derives from its focused design and improved capabilities targeting product-grade and modern robotic systems. Following ROS 2's example…
Benjamin Felbrich, Tim Schork, Achim Menges
The objective of autonomous robotic additive manufacturing for construction in the architectural scale is currently being investigated in parts both within the research communities of computational design and robotic fabrication (CDRF) and deep reinforcement learning (DRL) in robotics. The presented study summarizes…
Sebastijan Veselic, Claudio Zito, Dario Farina
Designing robotic assistance devices for manipulation tasks is challenging. This work aims at improving accuracy and usability of physical human-robot interaction (pHRI) where a user interacts with a physical robotic device (e.g., a human operated manipulator or exoskeleton) by transmitting signals which need to be…
Jeremy Gordon, John Chuang, Giovanni Pezzulo
The task of planning future actions in the context of an uncertain world results in massive state spaces that preclude exhaustive search and other strategies explored in the domains of both human decision-making and computational agents. One plausible solution to this dimensionality explosion is to decompose the task…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Adam Matić, Pavle Valerjev, Alex Gomez-Marin
The control architecture guiding simple movements such as reaching toward a visual target remains an open problem. The nervous system needs to integrate different sensory modalities and coordinate multiple degrees of freedom in the human arm to achieve that goal. The challenge increases due to noise and transport…
Niamh Ellis, Alexander Caravaggio, Jonathan Kelly, Ian Young + 2 more
Robotics looks to nature for inspiration to perform effectively in unstructured environments and can be used as a platform to test biological hypotheses. Social animals often share information about food source locations: one example is tandem running in ants, where a leader guides a naive recruit to a known profitable…
Riley Hickman, Malcolm Sim, Sergio Pablo-García, Ivan Woolhouse + 6 more
Self-driving laboratories (SDLs) are next-generation research and development platforms for closed-loop, autonomous experimentation that combine ideas from artificial intelligence, robotics, and high-performance computing. A critical component of SDLs is the decision-making algorithm used to prioritize experiments to…
Moritz J. F. Krusche, Eric Schulz, Arthur Guez, Maarten Speekenbrink
How do people plan ahead when searching for rewards? We investigate planning in a foraging task in which participants search for rewards on an infinite two-dimensional grid. Our results show that their search is best-described by a model which searches approximately 3 steps ahead. Furthermore, participants do not seem…
Jeremy Gordon, John Chuang, Giovanni Pezzulo
The task of planning future actions in the context of an uncertain world results in massive state spaces that preclude exhaustive search and other strategies explored in the domains of both human decision-making and computational agents. One plausible solution to this dimensionality explosion is to decompose the task…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Eric Leonardis, Leo Breston, Rhiannon Lucero-Moore, Leigh Sena + 6 more
Interactive neurorobotics is a subfield which characterizes brain responses evoked during interaction with a robot, and their relationship with the behavioral responses. Gathering rich neural and behavioral data from humans or animals responding to agents can act as a scaffold for the design process of future social…
Authors not listed
Computer-aided synthesis planning aims to identify viable synthetic routes from a target compound to readily available building blocks by iteratively decomposing molecules into smaller precursors. Self-play search algorithms, trained with simulated experience, reach state-of-the-art performance. However, these methods…