diff --git a/config.toml b/config.toml index 3773b69..73b893c 100644 --- a/config.toml +++ b/config.toml @@ -199,6 +199,12 @@ theme = 'mainroad' url = "/program/accepted" parent = "program" weight = "15" + [[menu.main]] + identifier = "awards" + name = "Awards" + url = "/program/awards" + parent = "program" + weight = 20 # [[menu.main]] # identifier = "doctoral-consortium" # name = "Doctoral Consortium" diff --git a/content/attending/accommodation.md b/content/attending/accommodation.md index 15fc8a7..9f48669 100644 --- a/content/attending/accommodation.md +++ b/content/attending/accommodation.md @@ -8,7 +8,7 @@ draft: false Please book your accommodation as soon as possible! Banff is a popular tourist destination and affordable accommodation will become harder to find the longer you leave it. -At this point, Banff Centre accommodations are sold out. +A small number of rooms have been made available on a first-come first-served basis. For anyone looking for a room, there are many [hotels in Banff](https://www.google.com/travel/search?q=hotels%20in%20banff&g2lb=2503771%2C2503781%2C4284970%2C4291517%2C4814050%2C4874190%2C4893075%2C4899571%2C4899572%2C4965990%2C72277293%2C72302247%2C72317059%2C72406588%2C72414906%2C72421566%2C72458066%2C72462234%2C72470440%2C72470899%2C72471280%2C72472051%2C72473841%2C72481458%2C72485656%2C72485658%2C72486593%2C72494250%2C72513422%2C72513513%2C72520082%2C72523972%2C72534000%2C72536387%2C72538597%2C72549171%2C72561422%2C72562082&hl=en-CA&gl=ca&ssta=1&ts=CAESCgoCCAMKAggDEAAaeApaEi4yJTB4NTM3MGNhNDU5MTBjNGFmZDoweGNhYWZhZWJlZGFhYzk0NjM6BUJhbmZmGigKEgnjQN2j5JRJQBE805ko4eVcwBISCawzLpGlmElAETzTmbAg4lzAEhoSFAoHCOgPEAYYAhIHCOgPEAYYBxgFMgIIASoHCgU6A0NBRA&qs=CAE4BkgAWksIATJHqgFEEAEqCiIGaG90ZWxzKAAyHxABIhtlpaL1L4HDj3whs-QjzdPCXcKJMiE7_Z5XrUMyExACIg9ob3RlbHMgaW4gYmFuZmY&ap=KigKEgnjQN2j5JRJQBE805ko4eVcwBISCawzLpGlmElAETzTmbAg4lzAMAJoAQ&ictx=1&ved=0CAAQ5JsGahcKEwiYiNbkk7iFAxUAAAAAHQAAAAAQCQ) but most tend to be expensive. In such cases we recommend sharing a room -- you can coordinate to find a roomate on the [Planning Slack Channel](https://slack.planning.domains/), #icaps24-shared-housing. Check AirBnB and hostels for other accommodations. diff --git a/content/home/index.md b/content/home/index.md index b75730e..ff34cb9 100644 --- a/content/home/index.md +++ b/content/home/index.md @@ -4,13 +4,13 @@ date: 2022-08-17T09:57:19+02:00 draft: false --- -The International Conference on Automated Planning and Scheduling (ICAPS) is the premier forum for exchanging news and research results on the theory and applications of intelligent and automated planning and scheduling technology. ICAPS 2024 is part of the [ICAPS conference series](https://www.icaps-conference.org/conference-series/). ICAPS 2024, the 34th International Conference on Automated Planning and Scheduling, will take place in Banff, Alberta, Canada in June 2024. +The International Conference on Automated Planning and Scheduling (ICAPS) is the premier forum for exchanging news and research results on the theory and applications of intelligent and automated planning and scheduling technology. ICAPS 2024 is part of the [ICAPS conference series](https://www.icaps-conference.org/conference-series/). ICAPS 2024, the 34th International Conference on Automated Planning and Scheduling, will take place in Banff, Alberta, Canada in June 2024. The ICAPS 2024 program committee invites paper submissions related to automated planning and scheduling. Relevant contributions include, but are not limited to: - Theoretical and empirical studies of planning and scheduling problems and algorithms; - Novel techniques and approaches that extend the scope and scale of problems that can be solved; -- Analytic and implemented tools supporting automated planning and scheduling; +- Analytic and implemented tools supporting automated planning and scheduling; - Studies of applying automated planning and scheduling technologies to significant problems with deep technical insight. {{< sponsors_table_json "/data/sponsors/sponsors.json" >}} ---> \ No newline at end of file diff --git a/content/program/awards.md b/content/program/awards.md new file mode 100644 index 0000000..2161b74 --- /dev/null +++ b/content/program/awards.md @@ -0,0 +1,38 @@ +--- +title: "Awards" +date: 2024-04-25T13:27:57+02:00 +draft: false +--- + +# ICAPS 2024 Awards + +### Best Paper Award + +[**Decoupled Search for the Masses: A Novel Task Transformation for Classical Planning**](https://openreview.net/forum?id=sqzyJpjsha) *David Speck, Daniel Gnad* + +### Best Paper Award (Runner-up) + +[**Expressiveness of Graph Neural Networks in Planning Domains**](https://openreview.net/forum?id=pKEkSAPSGJ) *Rostislav Horcik, Gustav Šír* + +### Best Student Paper Award + +[**Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences**](https://openreview.net/forum?id=aq8LOMsYgc) *Yorai Shaoul, Itamar Mishani, Maxim Likhachev, Jiaoyang Li* + +### Best Student Paper Award (Runner-up) + +[**A Counter-Example Based Approach to Probabilistic Conformant Planning**](https://openreview.net/forum?id=gg527bL2Oi) *Xiaodi Zhang, Alban Grastien, Charles Gretton* + +---- + +### ICAPS Influential Paper Award + +*To be announced soon...* + +### ICAPS Best Dissertation Award + +*To be announced soon...* + +### ICAPS Best Dissertation Award (Runner-up) + +*To be announced soon...* + diff --git a/data/accepted/accepted.json b/data/accepted/accepted.json index 7264f34..1872a82 100644 --- a/data/accepted/accepted.json +++ b/data/accepted/accepted.json @@ -2,7 +2,7 @@ "tracks": [ { "number": 1, - "name": "Long Paper", + "name": "Long Papers", "papers": [ { "number": 164, @@ -221,7 +221,7 @@ }, { "number": 89, - "title": "Exact Multi-objective Path Finding with Negative Weights and Negative Cycles", + "title": "Exact Multi-objective Path Finding with Negative Weights and Negative Cycles", "abstract": "The point-to-point Multi-objective Shortest Path (MOSP) problem is a classic yet challenging task that involves finding all Pareto-optimal paths between two points in a graph with multiple edge costs. Recent studies have shown that employing A* search can lead to state-of-the-art performance in solving point-to-point MOSP instances with non-negative costs. In this paper, we propose a novel A*-based multi-objective search framework that not only handles graphs with negative costs and even negative cycles but also incorporates multiple speed-up techniques to enhance the efficiency of exhaustive search with A*. Through extensive experiments on large realistic test cases, our algorithm demonstrates remarkable success in solving difficult MOSP instances, outperforming the state of the art by up to an order of magnitude.", "authors": "Saman Ahmadi, Daniel Harabor, Nathan R. Sturtevant, Mahdi Jalili", "url": "https://openreview.net/forum?id=sBHklMlq0c", @@ -919,7 +919,7 @@ }, { "number": 2, - "name": "Short Paper", + "name": "Short Papers", "papers": [ { "number": 173, @@ -1052,6 +1052,108 @@ "tldr": "" } ] + }, + { + "number": 3, + "name": "Previously Published Papers", + "papers" : [ + { + "number": 1001, + "title": "Effort Level Search in Infinite Completion Trees with Application to Task-and-Motion Planning", + "abstract": "Solving a Task-and-Motion Planning (TAMP) problem can be represented as a sequential (meta-) decision process, where early decisions concern the skeleton (sequence of logic actions) and later decisions concern what to compute for such skeletons (e.g., action parameters, bounds, RRT paths, or full optimal manipulation trajectories). We consider the general problem of how to schedule compute effort in such hierarchical solution processes. More specifically, we introduce infinite completion trees as a problem formalization, where before we can expand or evaluate a node, we have to solve a preemptible computational sub-problem of a priori unknown compute effort. Infinite branchings represent an infinite choice of random initializations of computational sub-problems. Decision making in such trees means to decide on where to invest compute or where to widen a branch. We propose a heuristic to balance branching width and compute depth using polynomial level sets. We show completeness of the resulting solver and that a round robin baseline strategy used previously for TAMP becomes a special case. Experiments confirm the robustness and efficiency of the method on problems including stochastic bandits and a suite of TAMP problems, and compare our approach to a round robin baseline. An appendix comparing the framework to bandit methods and proposing a corresponding tree policy version is found on the supplementary webpage: https://www.user.tu-berlin.de/mtoussai/24-CompletionTrees/.", + "authors": "Marc Toussaint, Joaquim Ortiz-Haro, Valentin N. Hartmann, Erez Karpas, Wolfgang Hönig", + "venue": "ICRA", + "url": "https://www.user.tu-berlin.de/mtoussai/24-CompletionTrees/", + "pdf": "https://www.user.tu-berlin.de/mtoussai/24-CompletionTrees/24-toussaint-ICRA.pdf", + "primary_keywords": ["Robotics"], + "long": false, + "tldr": "TAMP problems are typically solved in steps, by solving a series of sub-problems. We to address the decision problem of where to invest compute when searching over possible sequences of sub-problems." + }, + { + "number": 1002, + "title": "When Prolog Meets Generative Models: a New Approach for Managing Knowledge and Planning in Robotic Applications", + "abstract": "In this paper, we propose a robot oriented knowledge representation system based on the use of the Prolog language. Our framework hinges on a special organisation of Knowledge Base (KB) that enables: 1) its efficient population from natural language texts using semi-automated procedures based on Large Language Models (LLMs); 2) the seamless generation of temporal parallel plans for multi-robot systems through a sequence of transformations; 3) the automated translation of the plan into an executable formalism. The framework is supported by a set of open source tools and its functionality is shown with a realistic application.", + "authors": "Enrico Saccon, Ahmet Tikna, Davide De Martini, Edoardo Lamon, Luigi Palopoli, Marco Roveri", + "venue": "ICRA", + "url": "https://arxiv.org/abs/2309.15049", + "pdf": "https://arxiv.org/pdf/2309.15049.pdf", + "primary_keywords": ["Knowledge Representation/Engineering"], + "long": false, + "tldr": "" + }, + { + "number": 1003, + "title": "Dynamic Targeting to Improve Earth Science Missions", + "abstract": "Dynamic targeting (DT) is an emerging concept in which data from a lookahead instrument are used to intelligently reconfigure and point a primary instrument to enhance science return. For example, in the smart ice hunting radar (Smart Ice Cloud Sensing project), a forward-looking radiometer is used to detect deep convective ice storms, which are then targeted using a radar. In other concepts, forward-looking sensors are used to detect clouds so that a primary sensor can avoid them. To this end, we have developed several algorithms from operations research and an artificial intelligence/heuristic search to point/reconfigure the dynamic instrument. We present simulation studies of DT for these concepts and benchmark these algorithms to show that DT is a powerful tool with the potential to significantly improve instrument science yield.", + "authors": "Alberto Candela, Jason Swope, Steve A. Chien", + "venue": "Aerospace Research Central", + "url": "https://arc.aiaa.org/doi/10.2514/1.I011233", + "pdf": "candela.pdf", + "primary_keywords": ["Applications"], + "long": false, + "tldr": "" + }, + { + "number": 1004, + "title": "Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds", + "abstract": "Task planning systems have been developed to help robots use human knowledge (about actions) to complete long-horizon tasks. Most of them have been developed for “closed worlds” while assuming the robot is provided with complete world knowledge. However, the real world is generally open, and the robots frequently encounter unforeseen situations that can potentially break theplanner’s completeness. Could we leverage the recent advances on pre-trained Large Language Models (LLMs) to enable classical planning systems to deal with novel situations? This paper introduces a novel framework, called COWP, for open-world task planning and situation handling. COWP dynamically augments the robot’s action knowledge, including the preconditions and effects of actions, with task-oriented commonsense knowledge. COWP embraces the openness from LLMs, and is grounded to specific domains via action knowledge. For systematic evaluations, we collected a dataset that includes 1085 execution-time situations. Each situation corresponds to a state instance wherein a robot is potentially unable to complete a task using a solution that normally works. Experimental results show that our approach outperforms competitive baselines from the literature in the success rate of service tasks. Additionally, we have demonstrated COWP using a mobile manipulator. Supplementary materials are available at: https://cowplanning.github.io/", + "authors": "Yan Ding, Xiaohan Zhang, Saeid Amiri, Nieqing Cao, Hao Yang, Andy Kaminski, Chad Esselink, Shiqi Zhang", + "venue": "Autonomous Robotics", + "url": "https://link.springer.com/article/10.1007/s10514-023-10133-5", + "pdf": "https://arxiv.org/abs/2305.17590.pdf", + "primary_keywords": [], + "long": false, + "tldr": "" + }, + { + "number": 1005, + "title": "Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Probabilistic Programs", + "abstract": "We describe AOS, the first general-purpose system for model-based control of autonomous robots using AI planning that fully supports partial observability and noisy sensing. The AOS provides a code-based language for specifying a generative model of the system, making", + "authors": "Or Wertheim, Dan R. Suissa, Ronen I. Brafman", + "venue": "IEEE Robotics and Automation", + "url": "https://arxiv.org/abs/2207.09713", + "pdf": "https://arxiv.org/abs/2207.09713.pdf", + "primary_keywords": [], + "long": false, + "tldr": "" + }, + { + "number": 1006, + "title": "Right Place, Right Time: Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty", + "abstract": "For many multi-robot problems, tasks are announced during execution, where task announcement times and locations are uncertain. To synthesise multi-robot behaviour that is robust to early announcements and unexpected delays, multi-robot task allocation methods must explicitly model the stochastic processes that govern task announcement. In this paper, we model task announcement using continuous-time Markov chains which predict when and where tasks will be announced. We then present a task allocation framework which uses the continuous-time Markov chains to allocate tasks proactively, such that robots are near or at the task location upon its announcement. Our method seeks to minimise the expected total waiting duration for each task, ie the duration between task announcement and a robot beginning to service the task. Our framework can be applied to any multi-robot task allocation problem where robots complete spatiotemporal tasks which are announced stochastically. We demonstrate the efficacy of our approach in simulation, where we outperform baselines which do not allocate tasks proactively, or do not fully exploit our task announcement models.", + "authors": "Charlie Street, Bruno Lacerda, Manual Mühlig, Nick Hawes", + "venue": "JAIR", + "url": "https://www.jair.org/index.php/jair/article/view/15057", + "pdf": "https://dl.acm.org/doi/pdf/10.1613/jair.1.15057", + "primary_keywords": ["Temporal Planning"], + "long": false, + "tldr": "" + }, + { + "number": 1007, + "title": "EELS: Autonomous snake-like robot with task and motion planning capabilities for ice world exploration", + "abstract": "Ice worlds are at the forefront of astrobiological interest because of the evidence of subsurface oceans. Enceladus in particular is unique among the icy moons because there are known vent systems that are likely connected to a subsurface ocean, through which the ocean water is ejected to space. An existing study has shown that sending small robots into the vents and directly sampling the ocean water is likely possible. To enable such a mission, NASA’s Jet Propulsion Laboratory is developing a snake-like robot called Exobiology Extant Life Surveyor (EELS) that can navigate Enceladus’ extreme surface and descend an erupting vent to capture unaltered liquid samples and potentially reach the ocean. However, navigating to and through Enceladus’ environment is challenging: Because of the limitations of existing orbital reconnaissance, there is substantial uncertainty with respect to its geometry and the physical properties of the surface/vents; communication is limited, which requires highly autonomous robots to execute the mission with limited human supervision. Here, we provide an overview of the EELS project and its development effort to create a risk-aware autonomous robot to navigate these extreme ice terrains/environments. We describe the robot’s architecture and the technical challenges to navigate and sense the icy environment safely and effectively. We focus on the challenges related to surface mobility, task and motion planning under uncertainty, and risk quantification. We provide initial results on mobility and risk-aware task and motion planning from field tests and simulated scenarios.", + "authors": "T. S. Vaquero, G. Daddi, R. Thakker, M. Paton, A. Jasour, M. P. Strub, R. M. Swan, R. Royce, M. Gildner, P. Tosi, M. Veismann, P. Gavrilov, E. Marteau, J. Bowkett, D. Loret de Mola Lemus, Y. Nakka, B. Hockman, A. Orekhov, T. D. Hasseler, C. Leake, B. Nuernberger, P. Proença, W. Reid, W. Talbot, N. Georgiev, T. Pailevanian, A. Archanian, E. Ambrose, J. Jasper, R. Etheredge, C. Roman, D. Levine, K. Otsu, S. Yearicks, H. Melikyan, R. R. Rieber, K. Carpenter, J. Nash, A. Jain, L. Shiraishi, M. Robinson, M. Travers, H. Choset, J. Burdick, A. Gardner, M. Cable, M. Ingham, M. Ono", + "venue": "Science Robotics Journal", + "url": "https://www.science.org/doi/10.1126/scirobotics.adh8332", + "pdf": "https://www.science.org/doi/epdf/10.1126/scirobotics.adh8332", + "primary_keywords": ["Applications"], + "long": false, + "tldr": "" + }, + { + "number": 1008, + "title": "Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree", + "abstract": "The Combined Target-Assignment and Path- Finding (TAPF) problem requires simultaneously assigning targets to agents and planning collision-free paths for them from their start locations to their assigned targets. As a leading approach to addressing TAPF, Conflict-Based Search with Target Assignment (CBS-TA) leverages K-best target assignments to create multiple search trees and Conflict-Based Search (CBS) to resolve collisions in each tree. While CBS- TA finds optimal solutions, it faces scalability challenges due to the duplicated collision resolution in multiple trees and the expensive computation of K-best assignments. We introduce Incremental Target Assignment CBS (ITA-CBS) to bypass these two computational bottlenecks. ITA-CBS generates only a single search tree and avoids computing K-best assignments by incrementally computing new 1-best assignments during the search. We show that ITA-CBS, in theory, is guaranteed to find optimal solutions and, in practice, runs faster than CBS-TA in 96.1% of 6,334 test cases.", + "authors": "Yimin Tang, Zhongqiang Ren, Jiaoyang Li, Katia Sycara", + "venue": "MRS", + "url": "https://arxiv.org/abs/2307.00663", + "pdf": "https://arxiv.org/abs/2307.00663.pdf", + "primary_keywords": [], + "long": false, + "tldr": "" + } + ] } ] } diff --git a/data/keynotes/keynotes.json b/data/keynotes/keynotes.json index be0d170..a276a56 100644 --- a/data/keynotes/keynotes.json +++ b/data/keynotes/keynotes.json @@ -1,23 +1,26 @@ { "keynotes": [ - { - "name": "Julie Shah", - "img" : "/img/keynotes/julie.shah.png", - "title": "Effective Human-Machine Partnerships in High Stakes Settings", - "webpage" : "https://interactive.mit.edu/about/people/julie", - "abstract": "Every team has top performers -- people who excel at working in a team to find the right solutions in complex, difficult situations. These top performers include nurses who run hospital floors, emergency response teams, air traffic controllers, and factory line supervisors. While they may outperform the most sophisticated optimization and scheduling algorithms, they cannot often tell us how they do it. Similarly, even when a machine can do the job better than most of us, it can’t explain how. The result is often an either/or choice between human and machine - resulting in what we call zero-sum automation. In this talk I present research case studies from industry and also share our lab's latest research effectively blending the unique decision-making strengths of humans and intelligent machines.", - "bio": "Julie Shah is the H.N. Slater Professor and Head of Aeronautics and Astronautics, faculty director of MIT's Industrial Performance Center, and director of the Interactive Robotics Group, which aims to imagine the future of work by designing collaborative robot teammates that enhance human capability. She is expanding the use of human cognitive models for artificial intelligence and has translated her work to manufacturing assembly lines, healthcare applications, transportation and defense. Before joining the faculty, she worked at Boeing Research and Technology on robotics applications for aerospace manufacturing. Prof. Shah has been recognized by the National Science Foundation with a Faculty Early Career Development (CAREER) award and by MIT Technology Review on its 35 Innovators Under 35 list. She was also the recipient of the 2018 IEEE RAS Academic Early Career Award for contributions to human-robot collaboration and transition of results to real world application. She has received international recognition in the form of best paper awards and nominations from the ACM/IEEE International Conference on Human-Robot Interaction, the American Institute of Aeronautics and Astronautics, the Human Factors and Ergonomics Society, the International Conference on Automated Planning and Scheduling, and the International Symposium on Robotics. She earned degrees in aeronautics and astronautics and in autonomous systems from MIT and is co-author of the book, What to Expect When You're Expecting Robots: The Future of Human-Robot Collaboration (Basic Books, 2020)." - }, { "name": "Hector Geffner", + "date": "June 4th", "img" : "/img/keynotes/hector.geffner.jpg", "title": "Learning Representations to Act and Plan", "webpage" : "https://www-i6.informatik.rwth-aachen.de/~hector.geffner/", "abstract": "Recent progress in deep learning and deep reinforcement learning (DRL) has been truly remarkable, yet two important problems remain: structural policy generalization and policy reuse. The first is about getting policies that generalize in a reliable way; the second is about getting policies that can be reused and combined in a flexible, goal-oriented manner. The two problems are studied in DRL but only experimentally, and the results are not clear and crisp. In our work, we have tackled these problems in a slightly different manner, developing languages for expressing general policies, and methods for learning them using combinatorial and DRL approaches. We have also developed languages for expressing and learning lifted action models, general subgoal structures (sketches), and hierarchical polices. In the talk, I'll present the main ideas and results, and open challenges. This is joint work with Blai Bonet, Simon Stahlberg, Dominik Drexler, and other members of the RLeap team at RWTH and LiU.", "bio": "Hector Geffner is an Alexander von Humboldt Professor at RWTH Aachen University, Germany and a Guest Wallenberg Professor at Linköping University, Sweden. Before joining RWTH, he was an ICREA Research Professor at the Universitat Pompeu Fabra, Barcelona, Spain. Hector obtained a Ph.D. in Computer Science at UCLA in 1989 and then worked at the IBM T.J. Watson Research Center in New Work, and at the Universidad Simon Bolivar in Caracas. Distinctions for his work and the work of his team include the 1990 ACM Dissertation Award and three ICAPS Influential Paper Awards. Hector currently leads a project on representation learning for acting and planning (RLeap) funded by an ERC grant." }, + { + "name": "Julie Shah", + "date": "June 5th", + "img" : "/img/keynotes/julie.shah.png", + "title": "Effective Human-Machine Partnerships in High Stakes Settings", + "webpage" : "https://interactive.mit.edu/about/people/julie", + "abstract": "Every team has top performers -- people who excel at working in a team to find the right solutions in complex, difficult situations. These top performers include nurses who run hospital floors, emergency response teams, air traffic controllers, and factory line supervisors. While they may outperform the most sophisticated optimization and scheduling algorithms, they cannot often tell us how they do it. Similarly, even when a machine can do the job better than most of us, it can’t explain how. The result is often an either/or choice between human and machine - resulting in what we call zero-sum automation. In this talk I present research case studies from industry and also share our lab's latest research effectively blending the unique decision-making strengths of humans and intelligent machines.", + "bio": "Julie Shah is the H.N. Slater Professor and Head of Aeronautics and Astronautics, faculty director of MIT's Industrial Performance Center, and director of the Interactive Robotics Group, which aims to imagine the future of work by designing collaborative robot teammates that enhance human capability. She is expanding the use of human cognitive models for artificial intelligence and has translated her work to manufacturing assembly lines, healthcare applications, transportation and defense. Before joining the faculty, she worked at Boeing Research and Technology on robotics applications for aerospace manufacturing. Prof. Shah has been recognized by the National Science Foundation with a Faculty Early Career Development (CAREER) award and by MIT Technology Review on its 35 Innovators Under 35 list. She was also the recipient of the 2018 IEEE RAS Academic Early Career Award for contributions to human-robot collaboration and transition of results to real world application. She has received international recognition in the form of best paper awards and nominations from the ACM/IEEE International Conference on Human-Robot Interaction, the American Institute of Aeronautics and Astronautics, the Human Factors and Ergonomics Society, the International Conference on Automated Planning and Scheduling, and the International Symposium on Robotics. She earned degrees in aeronautics and astronautics and in autonomous systems from MIT and is co-author of the book, What to Expect When You're Expecting Robots: The Future of Human-Robot Collaboration (Basic Books, 2020)." + }, { "name": "Dale Schuurmans", + "date": "June 6th", "img" : "/img/keynotes/dale.schuurmans.jpg", "title": "Computing and Planning with Large Generative Models", "webpage": "https://webdocs.cs.ualberta.ca/~dale/", diff --git a/data/sponsors/sponsors.json b/data/sponsors/sponsors.json index 7a102c5..f6818db 100644 --- a/data/sponsors/sponsors.json +++ b/data/sponsors/sponsors.json @@ -1,16 +1,5 @@ { "levels": [ - { - "rank": "Platinum Partners", - "list": [ - { - "logo": "/img/partners/nsf.jpeg", - "height": "-1", - "width": "140", - "link": "https://www.nsf.gov" - } - ] - }, { "rank": "Gold Partners", "list": [ @@ -25,18 +14,6 @@ { "rank": "Silver Partners", "list": [ - { - "logo": "/img/partners/filuta-logo-symbol.svg", - "height": "-1", - "width": "140", - "link": "https://filuta.ai" - }, - { - "logo": "/img/partners/alicetechnologies.jpg", - "height": "-1", - "width": "140", - "link": "http://www.alicetechnologies.com" - }, { "logo": "/img/partners/jpmorgan.png", "height": "150", @@ -48,6 +25,12 @@ "height": "-1", "width": "140", "link": "http://psresearch.xyz" + }, + { + "logo": "/img/partners/porter.jpg", + "height": "-1", + "width": "200", + "link": "https://www.flyporter.com" } ] }, @@ -61,28 +44,22 @@ "link": "https://sift.net" }, { - "logo": "/img/partners/RSJ_logo.png", - "height": "-1", - "width": "200", - "link": "https://rsj.com/en" - }, - { - "logo": "/img/partners/sony.png", + "logo": "/img/partners/IBM_60_RGB.gif", "height": "-1", "width": "200", - "link": "https://ai.sony" + "link": "https://research.ibm.com/" }, { - "logo": "/img/partners/googlelogo_color_416x140dp.png", + "logo": "/img/partners/slb.png", "height": "-1", "width": "200", - "link": "https://research.google/" + "link": "https://www.slb.com/" }, { - "logo": "/img/partners/IBM_60_RGB.gif", + "logo": "/img/partners/filuta-logo-symbol.svg", "height": "-1", - "width": "200", - "link": "https://research.ibm.com/" + "width": "140", + "link": "https://filuta.ai" } ] } diff --git a/layouts/shortcodes/keynotes_table_json.html b/layouts/shortcodes/keynotes_table_json.html index 7ffe601..dbe11fb 100644 --- a/layouts/shortcodes/keynotes_table_json.html +++ b/layouts/shortcodes/keynotes_table_json.html @@ -4,6 +4,7 @@ {{ range $keynote := $data.keynotes }} +

({{ print $keynote.date }})

diff --git a/static/img/partners/porter.jpg b/static/img/partners/porter.jpg new file mode 100755 index 0000000..394110d Binary files /dev/null and b/static/img/partners/porter.jpg differ diff --git a/static/img/partners/slb.png b/static/img/partners/slb.png new file mode 100755 index 0000000..578666b Binary files /dev/null and b/static/img/partners/slb.png differ