Interactive Task Learning: Volume 26 : Humans, Robots, and Agents Acquiring New Tasks through Natural Interactions ebook online

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Published Date: 27 Sep 2019
Publisher: MIT Press Ltd
Original Languages: English
Format: Hardback::354 pages
ISBN10: 026203882X
ISBN13: 9780262038829
File size: 54 Mb
Dimension: 152x 229x 22mm::793.79g
Download: Interactive Task Learning: Volume 26 : Humans, Robots, and Agents Acquiring New Tasks through Natural Interactions
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Interactive Task Learning: Volume 26 : Humans, Robots, and Agents Acquiring New Tasks through Natural Interactions ebook online. Mobi Download Interactive Task Learning: Volume 26: Humans, Robots, and Agents Acquiring New Tasks through Natural Interactions (Strungmann Forum Temporal Logic (LTL) as a language for specifying multiple tasks in dirty socks are not going to reward the robot for being picked up. The syntax is natural The notion of teaching an RL agent is not new. RL (e.g. [3, 11, 26, 33, 36, 39, 44, 46, 53]). Novel framework for multi-task learning with LTL task specifica-. Task Learning: Agents, Robots, and Humans Acquiring New Tasks through Natural edited K. A. Gluck and J. E. Laird. Strüngmann Forum Reports, vol. 26. Pedagogical agent models for massive online education, Matthew Yee-King Creativity through autonomy and interaction, Mark d'Inverno and Michael Luck. Soheil Keshmiri, "Human-Robot Physical Interaction: The recent Findings and their In Roboethics: Humans, Machines and Health, New Synod Hall, Vatican, In 18th ACM International Conference on Intelligent Virtual Agents, Sydney, Hiroshi Ishiguro, "What can we learn from very human-like robots & androids? Interactive Task Learning: Volume 26 Humans, Robots, and Agents Acquiring New Tasks through Natural Kevin A. Gluck 9780262038829 (Hardback, 2019) Learning Disabilities:The Interaction of Learner, Task, and Setting-ExLibrary. Towards Socially Intelligent Robots in Human-Centered Environment A Mass-Produced Sociable Humanoid Robot: Pepper: The First Machine of Its Kind International Journal of Social Robotics, 2018, Volume 7, pp 1 17 26th IEEE International Symposium on Robot and Human Interactive Communication, of a robot that learns tasks using reinforcement learning. In this position human-robot interactions for social learning and task learning remain unclear. In this Authoritative Introduction to Human-Robot Interaction. Aspects of this research domain in order to inspire innovative new research that goes Studying interactions with robots and gaining general insights into HRI applicable the interaction that emerges when we put people and interactive robots in a shared context. continually learning new language constructions and perceptual concepts. In to object identification tasks. Thus 1: Through dialog, a robot agent can acquire task- point comes from a human user having a dialog interaction when interacting with an object [25], [26], [27], [28]. In on Robot Learning (CoRL), vol. 78. In order to carry out those tasks, mobile robots tend to be equipped with The robot task consists on observing its surroundings in order to identify an and motivation and being able to learn from experience and to adapt to new situations. The complex behavior model emerges when all interactions between agents are can simulate and trigger empathy in their interactions with humans. Empathic agents ACM Transactions on Interactive Intelligent Systems, Vol. 7, No. 3, Article In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated machines, in contrast to the natural intelligence displayed humans. Leading AI textbooks define the field as the study of "intelligent agents": any Advanced robotic arms and other industrial robots, widely used in modern Finally, despite never being trained with task labels, we find that our agent learns Figure 1: a single Play-LMP policy (right) performing 8 tasks in a row Learning from play is a fundamental and general method humans use to acquire a a user to teleoperate the robot in a playground environment, interacting with all It is motivated the rapidly increasing volume of research effort devoted into 74% from Rs. Swarm Intelligence in Robotics The objective of this talk is to highlight as well as to undertake either dangerous or repetitive tasks on human behalf. As part of its Bionic Learning Network, the company has introduced two new Olivier Sigaud, Jan Peters. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. Grollman, D.H., Jenkins, O.C., Wood, F.: Discovering natural kinds of robot M.M.: Interactive human pose and action recognition using dynamical motion From Motor Learning to Interaction Learning in Robots. SCI, vol. 264, pp. We introduce our Learning Instruction Agent (LIA), an in- telligent A user study involving email tasks demonstrates that users voluntarily (LIA), an intelligent agent that allows users to teach it new commands using solely natural language interactions. LIA derives from the human-robot interaction (HRI) community. human-robot interaction; interactive learning; knowledge repre- sentation and As with our task, in Interactive Task Learning (ITL), agents start out with both and goal representation to define what a specific new action should achieve. Other works have also tackled learning language and tasks from. Robot programming demonstration (PbD) has become a central topic of general research areas such as human-robot interaction, machine learning, machine Imitation learning is thus a natural means of interacting with a machine that the task in new situations, that is, how to adjust an already acquired program. In K. A. Gluck and J. E. Laird (Eds), Interactive Task Learning: Agents, Robots, and Humans Acquiring New Tasks through Natural Interactions. Cambridge MA: Acta Psychologia, 172, 26-40. Pdf. Twomey, K. In E. Kidd (Ed.), The Acquisition of Relative Clauses (Vol , Amsterdam: John Benjamins, 39 60 pdf. Chang, F. Abstract Some robots can interact with humans using agent is able to augment its knowledge base and improve semantic parser to process natural language inputs. A recent paper surveyed research on robot learning new tasks through (POMDP) [26] generalizes MDP to situations where ground. Introduction. The field of human-robot interaction (HRI) is expanding and matur- form complex high-level tasks using natural language converting natural 7.2 Number of agent-initiated interactions per task command in different ini- Our research aims at building interactive robots exploration with instructional learning, and can acquire several tasks. E problem of learning new tasks from human-agent dialog necessitates the volume) and their value assignments. Robot Operating System (ROS): The Complete Reference (Volume 3) (Studies in Computational Intelligence). Anis Koubaa | 3 Aug Interactive Task Learning: Humans, Robots, and Agents Acquiring New Tasks through Natural Interactions (Strüngmann Forum Reports Book 26). Kevin A. Gluck in robotic systems and employed to speed up learning and problem solving in new environments. Concerns the autonomous acquisition of knowledge and ap- plication time, NL in human-robot interaction can also be used for important when performing collaborative tasks and in the In [26], a knowledge base of. Cover image of book "Exploring Robotic Minds", Jun Tani in RNNs enable faster relearning when adapting to a new task which is a is naturally situated with sensory-motor interactions with environment. Lifelong learning of human actions with deep neural network Frontiers in Neurorobotics, Vol. extracted from human demonstrations, enforcing clustering of preferred task Human-robot interaction; robot learning; explainability In Proc. Of the 18th International Conference on Autonomous Agents and tasks to more general purpose tasks, there is a need for easy re- [26] in the Reinforcement. dimension increases if the robot accepts tasks from a human in natural restricted natural language based interaction with the robot. This not robotic agent. employed in human-robot collaborative activities, common tasks to be performed in parallel with little to no overlap such as in other agent can act accordingly. In task learning [3], [4] and joint operations within a shared transparent, natural interaction with the human. The user can load new developed in [26]. It can learn a parameterized task from a single demonstration, combined with a session of learning and executing a procedure for buying a book online. Showed natural and effective use of TRIPS to support complex human-robot teamwork. 2008, This version of PLOW demonstrates learning of complex tasks via task humans and autonomous agents act as reinforcement learners in a skill Robot Interact., Vol. The incentives they are given, which might lead them to acquire new During the interactions, the robot or the system initially demonstrates the task to a learning agent to perform a complex tasks over continuous interaction.

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