https://www.youtube.com/watch?v=Sigv9srzsrc
Autonomy Talks - 31/05/2022Speaker: Prof. Stephen Smith, University of WaterlooTitle: Learning Motion Plans and User Preferences for Robot AutonomyAbstract:

https://ethz.ch/content/dam/ethz/special-interest/mavt/dynamic-systems-n-control/idsc-dam/Research_Frazzoli/TuesdayTalks/smith.pdf
The second is in learning user preferences for desired robot behaviour. This is done by actively querying a user with different robot behaviours to elicit their preferences. For both these problems this talk will describe solution frameworks that combine learning, control and optimization to improve robot capabilities. Bio Stephen L. Smith is

https://www.youtube.com/@autonomytalks
Autonomy Talks are open to everyone and no registration is required. The goal of the talks is the exchange among different groups working on innovations in autonomy. Autonomy Talks will take place

https://ece.uwaterloo.ca/~sl2smith/publications.html
N. Wilde, D. Kulic, and S. L. Smith. Learning User Preferences in Robot Motion Planning through Interaction. In IEEE International Conference on Robotics and Automation, Brisbane, Australia, pages 619-626, May 2018. F. Imeson and S. L. Smith. Clustering in Discrete Path Planning for Approximating Minimum Length Paths.

https://robotics.cs.rutgers.edu/wafr2020/wp-content/uploads/sites/7/2020/05/WAFR_2020_FV_44.pdf
motion plan and the quality of the resulting plan increase with the number of primitives [1,8]. Thus we address the rst aspect by selecting a subset of the estimated trajectories with a given size.To estimate the user trajectories, we consider that the quality of a plan is evaluated by a user who has preferences with respect to the robot's

https://www.youtube.com/watch?v=ShslSy87tGQ
ICRA 2018 Spotlight VideoInteractive Session Tue AM Pod K.8Authors: Wilde, Nils; Kulic, Dana; Smith, Stephen L.Title: Learning User Preferences in Robot Moti

https://ece.uwaterloo.ca/~sl2smith/papers/2018ICRA-Learning_User.pdf
Learning User Preferences in Robot Motion Planning through Interaction Nils Wilde Dana Kulic Stephen L. Smith´ Abstract—In this paper we develop an approach for learn-ing user preferences for complex task specifications through human-robot interaction. We consider the problem of planning robot motion in a known environment, but where a user

https://research.monash.edu/en/publications/learning-user-preferences-in-robot-motion-planning-through-intera
T1 - Learning user preferences in robot motion planning through interaction. AU - Wilde, Nils. AU - Kulic, Dana. AU - Smith, Stephen L J. PY - 2018/9/10. Y1 - 2018/9/10. N2 - In this paper we develop an approach for learning user preferences for complex task specifications through human-robot interaction.

https://par.nsf.gov/servlets/purl/10420008
Keywords: Human-Robot Collaboration, Learning From Demonstration, Motion Planning, Reinforcement Learning 1. Introduction With the development of Industry 4.0, there is an increasing demand for robots to work adaptively and smartly with humans in industrial settings (El Zaatari et al., 2019). However, currently, motions of industrial

https://motionplanning.org/publications/hernandez2020increasing-robot-autonomy-via-motion.pdf
the work load from the user to the robot using. AR. coupled with motion planning techniques. The work in [24] introduces the idea of an object-based programming scheme using. AR. A context-based multimodal framework that allows the user to interact with the robot is presented based on which the user is able to convey objects of interest and

https://link.springer.com/article/10.1007/s00146-020-01133-5
1 Introduction. This Special Issue explores perceived autonomy in drones and robots and its broader implications for human living. Building on Lindemann and colleagues' ( 2016) Special Issue of AI and Society, we turn from considering human-machine relations as "idealized one-on-one interactions" to focusing on the "incorporation of

https://uwaterloo.ca/electrical-computer-engineering/events/ece-seminar-series-challenges-long-term-autonomy-professor
The second is in adapting robot behaviour to user preferences. This is done through actively querying a user with different robot behaviours to elicit their preferences. ... The talk will conclude with a discussion of some of the fundamental challenges that remain to achieve long-term autonomy. Biography: Stephen L. Smith received his B.Sc

https://picknik.ai/ros/robotics/autonomy/2021/06/14/Mark-Moll-Autonomy-Talk.html
PickNik's Director of Research, Dr. Mark Moll, gave a public Tech Talk at Galois this past May entitled "Toward Robot Autonomy: Tasks, Plans, and Policies." Dr. Moll discusses how tasks, plans, and policies determine outcomes of robotic behavior. Find the original post here at Galois.

https://www.semanticscholar.org/paper/Learning-User-Preferences-in-Robot-Motion-Planning-Wilde-Kuli%C4%87/1843993cdaf7d37c5e53cac4d8b5dd7807c25973
An algorithm is presented that iteratively builds a set of constraints on the relative importance of each user constraint, and it is proved that with sufficient interaction, the algorithm determines a user-optimal path. In this paper we develop an approach for learning user preferences for complex task specifications through human-robot interaction. We consider the problem of planning robot

https://arxiv.org/pdf/1805.07719v1.pdf
Abstract—Robotic agents that share autonomy with a human should leverage human domain knowledge and account for their preferences when completing a task. This extra knowledge can dramatically improve plan efficiency and user-satisfaction, but these gains are lost if communicating with a robot is taxing and unnatural.

https://arxiv.org/pdf/2206.10300
Riek. "Preference learning in assistive robotics: Observational repeated inverse reinforcement learning." In Machine Learning for Healthcare Conference, pp. 420-439. PMLR, 2018. [2] Wilde, Nils, Dana Kulic´, and Stephen L. Smith. "Learning user pref-erences in robot motion planning through interaction." In 2018 IEEE

https://galois.com/blog/2021/04/public-tech-talk-toward-robot-autonomy-tasks-plans-and-policies/
Robots are increasingly deployed outside of carefully controlled factory settings. Advances in robot motion planning have made it possible to compute feasible motions for complex systems. My work is focused on increasing the abstraction level and time horizon of the types of robot tasks that (a) can be specified, (b) are computationally tractable, and (c) can be successfully executed on

https://www.researchgate.net/profile/Tom-Williams-16/publication/381042691_Robot_Take_the_Joystick_Understanding_Space_Robotics_Experts'_Views_on_Autonomy/links/665aa127bc86444c72220124/Robot-Take-the-Joystick-Understanding-Space-Robotics-Experts-Views-on-Autonomy.pdf
autonomy thus interacts with human factors considerations to determine the success of robotic missions; a phenomenon that has been used to inform how robots adapt their level

https://link.springer.com/article/10.1007/s11023-021-09579-2
Social robots are robots that can interact socially with humans. As social robots and the artificial intelligence (AI) that powers them becomes more advanced, they will likely take on more social and work roles. This has many important ethical implications. In this paper, we focus on one of the most central of these, the impacts that social robots can have on human autonomy. We argue that, due

https://online.stanford.edu/courses/aa274a-principles-robot-autonomy-i
This course will cover the basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. You will learn algorithmic approaches for robot perception, localization, and simultaneous localization and mapping as well as the control of non-linear systems, learning-based control, and robot motion

https://arxiv.org/abs/2404.14547
Also, the framework assesses and records user preferences for non-ambiguous instructions, enhancing future interactions. The last part of this process is the translation of disambiguated instructions into a robot motion plan using Linear Temporal Logic. This paper details the development of this framework and the evaluation of its performance

https://idsc.ethz.ch/research-frazzoli/autonomy-talks.html
Autonomy Talks. Autonomy Talks are open to everyone and no registration is required. The goal of the talks is the exchange among different groups, inside and outside ETH Zürich, working on innovations in the broad fields of Autonomy, Robotics and Control. As of 2024, the official webpage for the seminar series will be this: gioele.science

https://www.ted.com/talks/devi_sahny_what_do_you_want_to_learn_today_the_power_of_autonomy_in_education
Devi argues that autonomy is a crucial ingredient for effective learning, and shares with us why she believes teachers should actively involve their students in the learning process. ... WATCH. TED Talks. Browse the library of TED talks and speakers. Playlists. 100+ collections of TED Talks, for curious minds. TED Series. Go deeper into