robot learning conference

M. Frank, A. Frster, J. Schmidhuber. Updates The conference proceedings have been published in the ACM Digital Library USC computer science faculty and students virtually presented their research in the field of intelligent robotics at the 4th Conference on Robotic Learning (CoRL), which ran Nov. 16 -18. This is the first time the annual international conference will be held in Australasia. In this talk, we will discuss a formalism for human-robot interaction built upon ideas from representation learning. We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. For instance, papers that have been submitted to the conference on Robot Learning (CoRL) 2020 can be submitted to our workshop. This discussion involves the presentation of current methods and the experiences made during algorithm deployment on real-world platforms. Reviews and Rebuttals will take place 15-28 August. . September 5, 2022 We welcome Intuitive into our lineup of sponsors. You'll learn how to link a Revit structural model to Robot Structural Analysis for structural steel code check and optimization. Toward Human-Like Robot Learning. You will be given access to gather.town to interact with the workshop attendees and present your work. Accepted papers and eventual supplementary material will be made available on the workshop website. We may receive compensation when you click on links to products we reviewed. AutoIncSFA and Vision-based Developmental Learning for Humanoid Robots. Advances in learning-based methods for perception, decision making, and control continue to open up new possibilities for deployment on physical robot platforms. Boston Dynamics' Stretch AI robot picks up a cup during the We Robot 2022 conference at the University of Washington in the US on September 15, 2022. International Conference on Learning Representations (2018). " Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learning ", Conference on Robot Learning, 2020 (*joint first authors) [ arXiv , paper webpage, 5-minute CoRL presentation video] E. Hannigan , B. September 20, 2022 We welcome Intrinsic and Bosch into our lineup of sponsors. For any faher questions, you can contact us at neuripswrl2020@robot-learning.ml. Leveraging such tools to obtain control policies is thus a seemingly promising direction. Robotics 2023 is a multifaceted educational forum and expo dedicated to addressing the issues involved with the design, development, manufacture and delivery of commercial robotics and intelligent systems products and services. International Conference on Robot Learning and Artificial Intelligence scheduled on June 15-16, 2023 at Toronto, Canada is for the researchers, scientists, scholars, engineers, academic, scientific and university practitioners to present research activities that might want to attend events, meetings, seminars, congresses, workshops, summit, and symposiums. Conference; 2021 The Twelfth International Conference on Swarm Intelligence (ICSI'2021), and 2020 International Conference on AI and Machine Learning. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. The local robotics community has advanced New Zealands global reputation in recent years, doubling or even tripling the number of papers that we have submitted to the major conferences and being recognised with international awards. These methods are applied to the ANYmal robot, a sophisticated medium-dog-sized quadrupedal system. Learning-based Control of a Legged Robot: Legged robots pose one of the greatest challenges in robotics. Nov 08 International Conference on Neural Network-Based Control and Robotics (ICNNBCR) - Istanbul, Turkey Nov 08 International Conference on Robotics, Automation, Control and Embedded Systems (ICRACES) - Dubai, United Arab Emirates Nov 08 International Conference on Machine Learning Applications in Robotics (ICMLAR) - Istanbul, Turkey A trio of researchers, including assistant professors Stefanos Nikolaidis and Jyo Deshmukh, developed a method that could allow robots to learn new tasks from observing a small number of demonstrationseven imperfect onesusing signal temporal logic (STL). The first meeting (CoRL 2017) was held in Mountain View, California on November 13 - 15, 2017, and brought together about 350 of the best researchers working on robotics and machine learning. Focus is on both applied and theoretical issues in robotics and automation. How can a robot learn to move objects around as humans do? In the meantime, you are required to create a poster and upload before Nov 24, 2020 AOE. The primary reason is that training with real robots, particularly with dynamically balancing systems, is complicated and expensive. The Conference on Robot Learning (CoRL) is an annual international conference aiming to bring together the robotics and machine learning research communities. Robotics Conferences is an indexed listing of upcoming meetings, seminars, congresses, workshops, programs, continuing CME courses, trainings, summits, and weekly, annual or monthly symposiums. and include the links in the paper. Open API. The Conference on Robot Learning (CoRL) is an annual international conference focusing on the intersection of robotics and machine learning. Workshop Proposals are due 15 July. This paper reviews recent research and development in the field of LfD. Can a paper be submitted to the workshop that has already appeared at a previous conference with published proceedings? This list of robotics conference is given in alphabetical order. If you have videos, code, or any other non-pdf materials, unfortunately, we cannot accept them. In alphabetical order they are: Karl Pertsch (University of Southern California)*; Youngwoon Lee (University of Southern California); Joseph J Lim (USC), Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments, Jun Yamada (University of Southern California); Youngwoon Lee (University of Southern California)*; Gautam Salhotra (University of Southern California); Karl Pertsch (University of Southern California); Max Pflueger (University of Southern California);GauravSukhatme (University of Southern California); Joseph J Lim (USC); Peter Englert (University of Southern California), Sim2Real Transfer for Deep Reinforcement Learning with Stochastic State Transition Delays The reviewing proces will be double blind, so please submit as anonymous by using \usepackage{neurips_wrl2020} in your main tex file. March 7-10, 2022 | Online (Originally Sapporo, Hokkaido, Japan) ACM/IEEE International Conference on Human-Robot Interaction (HRI) is the premium venue for publishing and presenting top-quality HRI research. Rather than merely focusing on applications of machine learning in robotics, as in the previous, successful iterations of the workshop, the new interdisciplinary panel will foster discussion on how real-world applications such as robotics can trigger various impactful directions for the development of machine learning and vice versa. AISTATS 2023 We invite submissions in all areas of robotics, including: mechanisms and design, robot learning, control and dynamics, planning, manipulation, field robotics, human-robot interaction, perception, formal . From robots that help with everyday tasks, move objects in complex environments, and learn on the job, USC computer scientists presented their research at 4th Conference on Robotic Learning. The robotics community will get the chance to meet again, exchange ideas and start new collaborations. Download Citation | On Jan 1, 2022, Robinson Jimenez Moreno and others published Audio Commands Recognition Through Deep Learning for Control Mobile Residential Assistant Robot | Find, read and . Ryan C Julian (University of Southern California)*; Benjamin Swanson (Google);GauravSukhatme (University of Southern California); Sergey Levine (Google); Chelsea Finn (Google Brain); Karol Hausman (Google Brain), Learning from Demonstrations using Signal Temporal Logic Description Chair Industry and Sponsorship. Open Access. 2022 International Symposium on Measurement and Control in Robotics (ISMCR), 5th International Conference on Control and Robots, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2022 10th International Conference on Control, Mechatronics and Automation (ICCMA), 2022 7th International Conference on Robotics and Automation Engineering (ICRAE), 2022 22nd International Conference on Control, Automation and Systems (ICCAS), 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids), 2022 Sixth IEEE International Conference on Robotic Computing (IRC), 2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI), 2022 7th International Conference on Mechanical Engineering and Robotics Research (ICMERR), 2022 7th International Conference on Control, Robotics and Cybernetics (CRC), 2023 9th International Conference on Automation, Robotics and Applications (ICARA), 2023 9th International Conference on Electrical Engineering, Control and Robotics (EECR), ACM/IEEE International Conference on Human-Robot Interaction, 2023 International Conference on Computer, Control and Robotics (ICCCR), 2023 IEEE International Conference on Robotics and Automation (ICRA). For instance, papers that have been submitted to the International Conference on Robotics and Automation (ICRA) 2023 or the International Conference on Learning Representations (ICLR) 2023 can be submitted to our workshop. Submissions for this Conference can be made by Jun 15, 2022.Authors can expect the result of submission by Sep 10, 2022. Yan Xu, Zhaoyang Huang, Kwan-Yee Lin, Xinge Zhu, Jianping Shi, Hujun Bao, Guofeng Zhang, Hongsheng Li ; Proceedings of the 2020 Conference on Robot Learning, PMLR 155:115-125 [ abs ] [ Download PDF] Learning 3D Dynamic Scene Representations for Robot Manipulation This past November, Google helped kickstart and host the first Conference on Robot Learning (CoRL) at our campus in Mountain View. e.g. But it is susceptible to imperfections in demonstrations and also raises safety concerns as robots may learn unsafe or undesirable actions. Sandeep Singh Sandha (UCLA)*; (USC Information Sciences Institute); Bharathan Balaji (Amazon); Fatima Anwar (University of Massachusetts, Amherst); Mani Srivastava (UC Los Angeles), Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning Summary : CoRL 2022 : Conference on Robot Learning will take place in Auckland, New Zealand.It's a 5 days event starting on Dec 14, 2022 (Wednesday) and will be winded up on Dec 18, 2022 (Sunday).. CoRL 2022 falls under the following areas: ROBOTICS, ROBOT LEARNING, etc. Open Recommendations. Shurjo Banerjee (University of Michigan)*; Jesse Thomason (University of Washington, incoming USC Spring 2021); Jason J Corso (University of Michigan), USC at AAAI 21: Algorithmic Fairness, Electoral College Strategy, De-Biasing Machine Learning, USC Researchers Present 30 Papers at NeurIPS 2021, Showing Robots How to Drive a CarIn Just A Few Easy Lessons, On the Cutting Edge: USC at the Robotic Science and Systems (RSS) Conference, USC Researchers Present 30 Papers at EMNLP 2021, USC at ICLR 2022: learning how to learn, decision making in complex environments, better forecasting models, Mork Family Department of Chemical Engineering and Materials Science, Sonny Astani Department of Civil and Environmental Engineering, Ming Hsieh Department of Electrical and Computer Engineering, Daniel J. Epstein Department of Industrial and Systems Engineering, Systems Architecting and Engineering Program, Departments and Major Research Institutes, Institute for Creative Technologies (ICT), Technology Innovation and Entrepreneurship, Coulter Translational Research Partnership Program, Viterbi Student Innovation Institute (VSI2), explored how robots can learn everyday tasks, Cognitive Learning for Vision and Robotics Lab (CLVR), this new method allows robots to learn from only a handful of demonstrations, combined motion planning and reinforcement learning, Cognitive Learning for Vision and Robotics Lab, Thomason and his colleagues present the RobotSlang Benchmark, The Reproducibility Crisis in Science These Researchers Have a Fix, Micro CT Scanner One of Just Two on the West Coast Comes to Campus, 34 new hires, including the new biomedical engineering chair, join for fall and spring semesters, Ming Hsieh Department of Electrical Engineering, Student Resources for Undergraduate Research. Robotics and Learning scheduled on March 16-17, 2023 in March 2023 in London is for the researchers, scientists, scholars, engineers, academic, scientific and university practitioners to present research activities that might want to attend events, meetings, seminars, congresses, workshops, summit, and symposiums. Specifically, we will first discuss the notion of latent strategies low dimensional representations sufficient for capturing non-stationary interactions. CoRL 2018 Proceedings are available as 87 volume of Proceedings of Machine Learning Research (PMLR) Experiments on physical platforms benefit from the complexity and variety of real-world data both for the generality of evaluation and richness of training data. June 2022 CoRL 2022 has received 504 paper submissions! Get conference alerts on upcoming conferences, meetings, seminars, workshops and other associated events in Robotics sector in 2022/2023. March 2022 We welcome our first sponsors: Google Research and Amazon. Dr Minas Liarokapis says hosting this conference will showcase New Zealands technical advancements in the robotics and automation fields and open opportunities to network and collaborate with global leaders. The proposed algorithm can efficiently and safely learn to pick up an object hidden inside a deep box and assemble a table in a cluttered environment. The first file is a PDF (< 20 Mb) and the second file is the PNG (<3MB with a resolution of at least 1000x560). We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Usually, these constraints are hand-designed by humans, which can be tedious. ACM/IEEE International Conference on Human-Robot Interaction: Stockholm, Sweden: March 14 to 15, 2023: MemCon: Silicon Valley, CA: March 15 to 16, 2023: Robotics and Autonomous Systems 90 (2017), 55-70. August 30, 2022 The travel and accommodation pages have been updated. (Organiser) Electrical and Computer Systems Engineering; Activity: Participating in or organising an event types Contribution to conference. Camera-ready paper deadline (Dec 4, 2020 AOE). IEEE Conference on Robotics and Automation (ICRA), 2022 Nitish Dashora, Daniel Shin, Dhruv Shah, Henry Leopold, David Fan, Ali Agha-Mohammadi, Nicholas Rhinehart, Sergey Levine [ arXiv] [ Website] ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning IEEE Conference on Robotics and Automation (ICRA), 2022 Sean Chen, Jensen Gao, Answer (1 of 4): I guess this list might help everyone - > 1. Can a submission to this workshop be submitted to another NeurIPS workshop in parallel? Gartner Data & Analytics Summit with this competition, our goal is to bring together researchers from different communities to (1) solicit novel and data-efficient robot learning algorithms, (2) establish a common forum to compare control and reinforcement learning approaches for safe robot decision making, and (3) identify the shortcomings or bottlenecks of the While current state-of-art methods need at least 100 demonstrations to nail a specific task, this new method allows robots to learn from only a handful of demonstrations. Instead of the common trade-offs between attracting a wider audience with well-known speakers and enabling early-stage researchers to voice their opinion, we encourage each of our senior presenters to share their presentations with a PhD student or postdoc from their lab. The team explored how robots can learn everyday tasks, like setting a table or cooking, by leveraging experience from solving other related tasks. Understanding, quantifying, and bridging the simulation to reality gap. Learning from demonstration (LfD) has been used to help robots to implement manipulation tasks autonomously, in particular, to learn manipulation behaviors from observing the motion executed by human demonstrators. The file names should be your paper ID. January 2022 We are excited to announce that CoRL 2022 will be held in Auckland, New Zealand. Topics will include general AI, strategy, privacy, Deep Learning, Machine Learning, data analytics, data science, big data, natural language processing (NLP) like Speech-to-Text, robotics, risk management, surveillance, and more. This talk will focus on how such obstacles can be overcome. Well get to showcase the work weve been doing, says Dr Liarokapis. Data-efficiency via transfer/multitask/meta learning. Our method uses manifold learning, a subfield of machine learning, to produce an artificial neural network that represents the constraint and whose local characteristics are enforced by the dataset of demonstrations, said co-author Isabel Rayas, a computer science Ph.D. student. We will also show that combining posterior parameter estimation and policy updates sequentially leads to further improvements on the convergence rate. This is a fantastic way that our local industry can create valuable connections, encourage investment, and build our reputation globally. Hosting the Conference on Robot Learning will open pathways to new opportunities," says Pereira. CoRL publishes significant original research at the intersection of robotics and machine learning. IEEE International Conference on Robotics and Automation, 2009. Boots [BibTeX] Learning Implicit Priors for Motion Optimization IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022 J. Urain, A. T. Le, A. Lambert, G. Chalvatzaki, B. Conference on Robot Learning (CoRL) is an annual international conference on robotics and machine learning. The biggest obstacle is the so-called reality gap discrepancies between the simulated and the real system. December is a summer month in New Zealand. Robot learning is a research field at the intersection of machine learning and robotics, which aims to build more intelligent systems. However, many challenges still remain when considering how robot learning can advance interactive agents such as robots that collaborate with humans. Aniruddh G Puranic (University of Southern California)*; Jyotirmoy Deshmukh (USC); Stefanos Nikolaidis (University of Southern California), Learning Equality Constraints for Motion Planning on Manifolds, Isabel M Rayas Fernndez (University of Southern California)*; Giovanni Sutanto (USC); Peter Englert (University of Southern California); Ragesh Kumar Ramachandran (University of Southern California);GauravSukhatme (University of Southern California), The RobotSlang Benchmark: Dialog-guided Robot Localization and Navigation Make sure to include your name and affiliations. Action-centric representations, on the other hand, can learn high-level planning, and do not have to explicitly instantiate objectness. All Conference Alert, trusted conference listing platform for academicians, industries & conference organizers, offers you complete details such as . USC Information Sciences Institute computer scientist, Luis Garcia, studied the reality gap in Deep Reinforcement Learning (RL) between simulations and real robots in an Amazon Science publication to improve the robustness of Deep RL policies. The Conference on Robot Learning (CoRL) is an annual international conference focusing on the intersection of robotics and machine learning. However, the development and evaluation of algorithmic progress are often constrained to simulation and rigid datasets, leading to overfitting to specific characteristics in these limited domains. Source credits: Driver demonstrations were provided through theUdacity Self-Driving Car Simulator. Here's the full list of top AI conferences to attend in 2022: 1. We are very thankful to our corporate sponsors, Naver Labs Europe and Google Brain, for enabling us to provide best paper awards and student registration fees. Primary: Learning and generalization of motor skills by learning from demonstration Peter Pastor, Heiko Hoffmann, Tamim Asfour, and Stefan Schaal. International Conference . Boots, and J. Peters [BibTeX] CoRL2018 - Conference on Robot Learning 2018 2018 edition of the Conference on Robot Learning was held in Zrich, Switzerland on Oct. 29-31 2018 DOCUMENTS FROM CoRL 2018. Submissions can be made at https://cmt3.research.microsoft.com/NEURIPSWRL2020/. Advertiser Disclosure: Unite.AI is committed to rigorous editorial standards to provide our readers with accurate information and news. like this where delegates from around the globe will exchange knowledge, ideas, and skills. To provide a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent solutions adopted in the fields of Robotics and Artificial Intelligence. We are excited about a new model for robotics, designed for generalization across diverse environments and instructions. April 2022 We welcome one more sponsor, Ambi Robotics. Since launching in 2017, CoRL has quickly become one of the worlds top academic gatherings at the intersection of robotics and machine learning, described as a selective, single-track conference for robot learning research, covering a broad range of topics spanning robotics, machine learning and control, and including theory and applications. In total, seven USC-affiliated papers were presented. CoRL 2022 will be held in Auckland, New Zealand from December 14 to 18, 2022. Reproducibility, reliability, and robustness. Thomason and his colleagues present the RobotSlang Benchmark, a corpus of human-human dialogs to localize and control a physical robot car in a search for target objects in a maze. However, you are encouraged to separately upload them to your own website, google drive, dropbox, github, youtube, etc. CoRL welcomes papers in areas such as: Submissions should focus on a core robotics problem and demonstrate . You can expect bright, sunny, long days and temperatures that range from 60F to 80F. 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Bled, Slovenia, 2011. 21. The uncertainty captured in the posterior can significantly improve the performance of reinforcement learning algorithms trained in simulation but deployed in the real world. AI Conference Deadlines Countdowns to top CV/NLP/ML/Robotics/AI conference deadlines. 6th International Conference on robotics and artificial intelligence will be held in Zurich, Switzerland on October 15th & 16th of 2021. The Conference and Workshop on Neural Information Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held every December. With more and more employers allowing flexible or remote schedules, these hybrid offices and hybrid classrooms need tools designed for this environment. CoRL is a selective, single-track international conference addressing theory and practice of machine learning for robots (and automation: where robot prototypes are scaled for cost effectiveness, efficiency, and reliability in practice). Domain adaptation including but not limited to: Sim-to-Real, Real-to-Sim, across multiple robotic platforms or environments. We are pleased to announce the 18th edition of the Robotics: Science and Systems Conference to be held in New York City in summer of 2022. . Welcome to the OpenReview homepage for CoRL 2022 Conference. Its expected to attract 600-700 robotics researchers from around the world to Auckland in December for a programme focused on the intersection of robotics and machine learning. Upcoming International Conferences in Robotics 2022 & 2023. Open Discussion. More News Group Job Openings Our group is regularly posting job openings ranging from internships to researcher positions. Graph attention networks. Robot learning and artificial intelligence Learning hierarchies or levels of representations Sensor and motor representations Learning of plans and control policies Integrating learning with control architectures Spatio-temporal representations designed for robot learning Developmental robotics and evolutionary-based learning approaches Above: Using the USC researchers method, an autonomous driving system would still be able to learn safe driving skills from watching imperfect demonstrations, such this driving demonstration on a racetrack. IEEE/RSJ International Conference on Intelligent Robots and Systems 5. Learning from demonstrations is becoming increasingly popular in obtaining effective robot control policies for complex tasks. Achin Jain, Adithyavairavan Murali, Akshara Rai, Alex Bewley, Ashvin Nair, Brian Ichter, Caterina Buizza, Coline Devin, Djalel Benbouzid, Dushyant Rao, Edward Johns, Jacob Varley, James Harrison, Jayesh Gupta, Jianwei Yang, Jie Tan, Johannes A. Stork, Jonathan Tompson, Karol Hausman, Kunal Menda, Marcin Andrychowicz, Marco Ewerton, Marko Bjelonic, Misha Denil, Nantas Nardelli, Nemanja Rakicevic, Octavio Antonio Villarreal Magaa, Panpan Cai, Peter Karkus, Raunak Bhattacharyya, Ruohan Wang, Sasha Salter, Siddharth Reddy, Spencer Richards, Takayuki Osa, Tomi Silander, Tuomas Haarnoja, Vikas Sindhwani, Walter Goodwin, Yevgen Chebotar, Yizhe Wu, Yunzhu Li. References do not count towards the limit of 4 pages. Date: October 23-27, 2022 Location: Kyoto, Japan The IROS is one of the largest and most impacting robotics research conferences worldwide. Making Hyper-parameters of Proximal Policy Optimization Robust to Time Discretization, Learning to solve multi-robot scheduling: mean-field inference theory for random GNN embedding and scalable auction with provable guarantee, Self-Supervised Policy Adaptation during Deployment, Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments, SAFARI: Safe and Active Robot Imitation Learning with Imagination, COG: Connecting New Skills to Past Experiences with Offline Reinforcement Learning, Model-based Policy Search for Partially Measurable Systems, State Representations in Robotics: Identifying Relevant Factors of Variation using Weak Supervision, Contextual Reinforcement Learning of Visuo-tactile Multi-fingered Grasping Policies, Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping, Multi-Robot Deep Reinforcement Learning via Hierarchically Integrated Models, Learning Visual-Locomotion Policies that Generalize to Diverse Environments, Structure Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems, Safe Sequential Exploration and Exploitation, Batch Exploration with Examples for Scalable Robotic Reinforcement Learning, Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones, Deep Affordance Foresight: Planning for What Can Be Done Next, Accelerating Reinforcement Learning with Learned Skill Priors, TACTO: A Simulator for Learning Control from Touch Sensing, Parrot: Data-driven Behavioral Priors for Reinforcement Learning, Transformer-based Meta-Imitation Learning for Robotic Manipulation, Efficient Exploration in Reinforcement Learning Leveraging Automated Planning, IV-SLAM: Introspective Vision for Simultaneous Localization and Mapping, Blending MPC & Value Function Approximation for Efficient Reinforcement Learning, Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency, Differentiable SLAM-nets: Learning Task-Oriented SLAM for Visual Navigation, Learning from Simulation, Racing in Reality, RMP2: A Differentiable Policy Class for Robotic Systems with Control-Theoretic Guarantees, AWAC: Accelerating Online Reinforcement Learning from Offline Datasets, use the NeurIPS Workshop template available here, https://cmt3.research.microsoft.com/NEURIPSWRL2020/, http://www.robot-learning.ml/2020/pptTemplate.pptx, http://www.robot-learning.ml/2020/neurips_wrl2020.sty, Invited talk 1 - Walking the Boundary of Learning and Interaction -, Contributed talk 1 - Accelerating Reinforcement Learning with Learned Skill Priors (Best Paper Runner-Up) -, Invited talk 2 - Object- and Action-Centric Representational Robot Learning -, Invited talk 3 - State of Robotics @ Google -, Invited talk 4 - Learning-based Control of a Legged Robot -, Contributed talk 2 - Multi-Robot Deep Reinforcement Learning via Hierarchically Integrated Models (Best Paper) -, Invited talk 5 - RL with Sim2Real in the loop/Online Domain Adaptation for Mapping -.

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