26 papers · ranked by Valyu relevance
Jiang Hua, Liangcai Zeng, Gongfa Li, Zhaojie Ju + 1 more
Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of…
Zexin Zheng, Jia-Feng Cai, Xiao-Ming Wu, Yi-Lin Wei + 2 more
'Wei-Shi Zheng'] The development of a generalist agent with adaptive multiple manipulation skills has been a long-standing goal in the robotics community. In this paper, we explore a crucial task, skill-incremental learning, in robotic manipulation, which is to endow the robots with the ability to learn new…
Zijian Liao, Qian Mao, Yichen Qin, Jinfeng Yuan + 1 more
Robots have enormous potential to assist humans in daily life. However, current robot development and popularization are impeded by its deficient functionality, lengthy task deployment, and vast experimental training. Poor flexibility, poor practicality, and poor universality in task accomplishment hinder robot…
Zhixin Jia, Mengxiang Lin, Zhuo Chen, Shibo Jian
— Vision-based learning methods provide promise for robots to learn complex manipulation tasks. However, how to generalize the learned manipulation skills to real-world interactions remains an open question. In this work, we study robotic manipulation skill learning from a single third-person view demonstration by…
Edgar Welte, Rania Rayyes
Dexterous manipulation is a crucial yet highly complex challenge in humanoid robotics, demanding precise, adaptable, and sample-efficient learning methods. As humanoid robots are usually designed to operate in human-centric environments and interact with everyday objects, mastering dexterous manipulation is critical…
Lin Shao, Toki Migimatsu, Jeannette Bohg
— Learning contact-rich, robotic manipulation skills is a challenging problem due to the high-dimensionality of the state and action space as well as uncertainty from noisy sensors and inaccurate motor control. To combat these factors and achieve more robust manipulation, humans actively exploit contact constraints in…
Qiushi Fu, Jason Y. Choi, Andrew M. Gordon, Mark Jesunathadas + 2 more
'Marco Santello' 'Nicholas P. Holmes'] Recent studies about sensorimotor control of the human hand have focused on how dexterous manipulation is learned and generalized. Here we address this question by testing the extent to which learned manipulation can be transferred when the contralateral hand is used and/or object…
Yinlin Li, Peng Wang, Rui Li, Mo Tao + 2 more
Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high…
Pegah Ojaghi, Romina Mir, Ali Marjaninejad, Andrew Erwin + 2 more
Reinforcement Learning of Object Manipulation Against Gravity Authors: ['Pegah Ojaghi' 'Romina Mir' 'Ali Marjaninejad' 'Andrew Erwin' 'Michael Wehner' 'Francisco J Valero-Cueva'] 1Computer Science and Engineering Department, University of California Santa Cruz, Santa Cruz, California, USA 2Department of Biomedical…
Lars Johannsmeier, Malkin Gerchow, Sami Haddadin
— In this paper we introduce a novel framework for expressing and learning force-sensitive robot manipulation skills. It is based on a formalism that extends our previous work on adaptive impedance control with meta parameter learning and compatible skill specifications. This way the system is also able to make use of…
Èric Pairet, Paola Ardón, Michael Mistry, Yvan Pétillot
In an attempt to confer robots with complex manipulation capabilities, dual-arm anthropomorphic systems have become an important research topic in the robotics community. Most approaches in the literature rely upon a great understanding of the dynamics underlying the system's behaviour and yet offer limited autonomous…
Haoran Sun, Linhan Yang, Yuping Gu, Jia Pan + 3 more
'Chaoyang Song' 'Liang Li'] Locomotion and manipulation are two essential skills in robotics but are often divided or decoupled into two separate problems. It is widely accepted that the topological duality between multi-legged locomotion and multi-fingered manipulation shares an intrinsic model. However, a lack of…
Alireza Barekatain, Hamed Habibi, Holger Voos
Manipulators in Manufacturing Authors: ['Alireza Barekatain' 'Hamed Habibi' 'Holger Voos'] This paper provides a structured and practical roadmap for practitioners to integrate Learning from Demonstration (LfD) into manufacturing tasks, with a specific focus on industrial manipulators. Motivated by the paradigm shift…
Jordana Ulloa-Marquez, Jennifer Gutterman, Marco Santello, Andrew M. Gordon
Successful object manipulation involves integrating object properties into a motor plan and scaling fingertip forces through learning. This study investigated whether learned manipulations using a two-digit grip transfer to a five-digit grip and vice versa, focusing on the challenges posed by added degrees of freedom…
Shlomi Haar, Guhan Sundar, A. Aldo Faisal
Motor-learning literature focuses on simple laboratory-tasks due to their controlled manner and the ease to apply manipulations to induce learning and adaptation. Recently, we introduced a billiards paradigm and demonstrated the feasibility of real-world-neuroscience using wearables for naturalistic full-body…
Catherine Anne Sager, Jackson Zenti, Michelle Marneweck
Visual feedback and prior experience support anticipatory force control, its learning, and online force adjustments during dexterous object manipulation. How proprioceptive reliability contributes to object manipulation remains poorly understood. Hybrid virtual reality (VR), which pairs physical object interaction with…
Yuanqi Du, Xian Liu, Shengchao Liu, Jieyu Zhang + 1 more
Discovering meaningful molecules in the vast combinatorial chemical space has been a longstanding challenge in many fields from materials science to drug discovery. Recent advances in machine learning, especially generative models, have made remarkable progress and demonstrate considerable promise for automated…
Christopher S Yang, Noah J Cowan, Adrian M Haith
How do people learn to perform tasks that require continuous adjustments of motor output, like riding a bicycle? People rely heavily on cognitive strategies when learning discrete movement tasks, but such time-consuming strategies are infeasible in continuous control tasks that demand rapid responses to ongoing sensory…
Yuanqi Du, Xian Liu, Shengchao Liu, Jieyu Zhang + 1 more
Discovering new structures in the chemical space is a long-standing challenge and has important applications to various fields such as chemistry, material science, and drug discovery. Deep generative models have been used in de novo molecule design to embed molecules in a meaningful latent space and then sample new…
Corson N. Areshenkoff, Anouk de Brouwer, Daniel J. Gale, Joseph Y. Nashed + 1 more
Motor learning is supported by multiple systems adapted to processing different forms of sensory information (e.g., reward versus error feedback), and by higher-order systems supporting strategic processes. Yet, the extent to which these systems recruit shared versus separate neural pathways is poorly understood. To…
Ian S. Howard, Laura Alvarez-Hidalgo
The human motor system exhibits remarkable plasticity: not only can we master complex skills, but we can also learn to control artificial effectors. Here, we examine whether haptic force feedback from a simulated endpoint mass facilitates the de novo learning of novel kinematic and dynamic mappings. We investigate this…
Shouvik Majumder, Koichi Hirokawa, Zidan Yang, Ronald Paletzki + 6 more
Neocortical spiking dynamics control aspects of behavior, yet how these dynamics emerge during motor learning remains elusive. Activity-dependent synaptic plasticity is likely a key mechanism, as it reconfigures network architectures that govern neural dynamics. Here, we examined how the mouse premotor cortex acquires…
Authors not listed
Digital twins are virtual companions for the design, scale-up, and control of chemical processes. Equipping digital twins with mechanistic models of their mirrored unit operation expands their range of applicability compared to pure data-driven models. As constructing mechanistic models requires time, effort, and…
Wei Zhang, Jonathan A Fine, Christopher Sculley, Jordon McGraw + 1 more
The representation of complex biomolecular structures and interactions is a difficult challenge across life sciences. Researchers and students use unintuitive 2D representations to gain an intuitive understanding of 3D space and molecular interactions. Since this is cumbersome for complex structures, such as…
Raphael Schween, Samuel D. McDougle, Mathias Hegele, Jordan A. Taylor
In recent years, it has become increasingly clear that a number of learning processes are at play in visuomotor adaptation tasks. In addition to the presumed formation of an internal model of the perturbation, learners can also develop explicit knowledge allowing them to select better actions in responding to a given…
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…