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Abstract: The development of flexible multifingered grippers with both adaptive grasping and in-hand manipulation capabilities remains a complex issue for human-like dexterous manipulation. After four decades of research in dexterous manipulation, many robotic hands have been developed. The development of these hands remains a key challenge, as the dexterity of robot hands is far from human capabilities. Through the evolution of robotics (from industrial and manufacturing robotics to service and collaborative robotics), the monograph details the evolution of the grasping function (from industrial grippers to dexterous robot hands) and the stakes inherent today to new robotic applications in open, dynamic environments. The aim of the monograph is to assist in the choice of a grasping and manipulation solution, taking into account both the design and control aspects, from the simplest industrial gripper to the most sophisticated multidigital hands. The increasing complexity of grasping function to meet flexibility challenges led to the development of control strategies based on theoretical approaches and data-based approaches using machine learning.Suggested CitationPascal Seguin, Célestin Preault, Philippe Bidaud and Jean-Pierre Gazeau (2023), "From Specialized Industrial Grippers to Flexible Grippers: Issues for Grasping and Dexterous Manipulation", Foundations and Trends® in Robotics: Vol. 11: No. 1, pp 1-89. http://dx.doi.org/10.1561/2300000074 PubDate: Mon, 17 Apr 2023 00:00:00 +020
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Abstract: No human drives a car in a vacuum; she/he must negotiate with other road users to achieve their goals in social traffic scenes. A rational human driver can interact with other road users in a socially-compatible way through implicit communications to complete their driving tasks smoothly in interaction-intensive, safety-critical environments. This monograph aims to review the existing approaches and theories to help understand and rethink the interactions among human drivers toward social autonomous driving. We take this survey to seek the answers to a series of fundamental questions: 1) What is social interaction in road traffic scenes' 2) How to measure and evaluate social interaction' 3) How to model and reveal the process of social interaction' 4) How do human drivers reach an implicit agreement and negotiate smoothly in social interaction' This monograph reviews various approaches to modeling and learning the social interactions between human drivers, ranging from optimization theory, deep learning, and graphical models to social force theory and behavioral and cognitive science. We also highlight some new directions, critical challenges, and opening questions for future research.Suggested CitationWenshuo Wang, Letian Wang, Chengyuan Zhang, Changliu Liu and Lijun Sun (2022), "Social Interactions for Autonomous Driving: A Review and Perspectives", Foundations and Trends® in Robotics: Vol. 10: No. 3-4, pp 198-376. http://dx.doi.org/10.1561/2300000078 PubDate: Thu, 24 Nov 2022 00:00:00 +010
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Abstract: Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of the robot’s behavior.In recent years, IIL has increasingly started to carve out its own space as a promising data-driven alternative for solving complex robotic tasks. The advantages of IIL are twofold, 1) it is data-efficient, as the human feedback guides the robot directly towards an improved behavior (in contrast with Reinforcement Learning (RL), where behaviors must be discovered by trial and error), and 2) it is robust, as the distribution mismatch between the teacher and learner trajectories is minimized by providing feedback directly over the learner’s trajectories (as opposed to offline IL methods such as Behavioral Cloning).Nevertheless, despite the opportunities that IIL presents, its terminology, structure, and applicability are not clear nor unified in the literature, slowing down its development and, therefore, the research of innovative formulations and discoveries.In this work, we attempt to facilitate research in IIL and lower entry barriers for new practitioners by providing a survey of the field that unifies and structures it. In addition, we aim to raise awareness of its potential, what has been accomplished and what are still open research questions.We organize the most relevant works in IIL in terms of human-robot interaction (i.e., types of feedback), interfaces (i.e., means of providing feedback), learning (i.e., models learned from feedback and function approximators), user experience (i.e., human perception about the learning process), applications, and benchmarks. Furthermore, we analyze similarities and differences between IIL and RL, providing a discussion on how the concepts offline, online, off-policy and on-policy learning should be transferred to IIL from the RL literature.We particularly focus on robotic applications in the real world and discuss their implications, limitations, and promising future areas of research.Suggested CitationCarlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari, Giovanni Franzese, Leandro de Souza Rosa, Ravi Prakash, Zlatan Ajanović, Marta Ferraz, Abhinav Valada and Jens Kober (2022), "Interactive Imitation Learning in Robotics: A Survey", Foundations and Trends® in Robotics: Vol. 10: No. 1-2, pp 1-197. http://dx.doi.org/10.1561/2300000072 PubDate: Tue, 22 Nov 2022 00:00:00 +010
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Abstract: Sampling-based methods are widely adopted solutions for robot motion planning. The methods are straightforward to implement, effective in practice for many robotic systems. It is often possible to prove that they have desirable properties, such as probabilistic completeness and asymptotic optimality. Nevertheless, they still face challenges as the complexity of the underlying planning problem increases, especially under tight computation time constraints, which impact the quality of returned solutions or given inaccurate models. This has motivated machine learning to improve the computational efficiency and applicability of Sampling- Based Motion Planners (SBMPs). This survey reviews such integrative efforts and aims to provide a classification of the alternative directions that have been explored in the literature. It first discusses how learning has been used to enhance key components of SBMPs, such as node sampling, collision detection, distance or nearest neighbor computation, local planning, and termination conditions. Then, it highlights planners that use learning to adaptively select between different implementations of such primitives in response to the underlying problem’s features. It also covers emerging methods, which build complete machine learning pipelines that reflect the traditional structure of SBMPs. It also discusses how machine learning has been used to provide data-driven models of robots, which can then be used by a SBMP. Finally, it provides a comparative discussion of the advantages and disadvantages of the approaches covered, and insights on possible future directions of research. An online version of this survey can be found at: https://prx-kinodynamic.github.ioSuggested CitationTroy McMahon, Aravind Sivaramakrishnan, Edgar Granados and Kostas E. Bekris (2022), "A Survey on the Integration of Machine Learning with Sampling-based Motion Planning", Foundations and Trends® in Robotics: Vol. 9: No. 4, pp 266-327. http://dx.doi.org/10.1561/2300000063 PubDate: Thu, 10 Nov 2022 00:00:00 +010
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