22 papers · ranked by Valyu relevance
Mengkang Hu, Yao Mu, Xinmiao Yu, Mingyu Ding + 6 more
'Wenqi Shao' 'Qiguang Chen' 'Bin Wang' 'Yu Qiao' 'Ping Luo'] This paper studies close-loop task planning, which refers to the process of generating a sequence of skills (a plan) to accomplish a specific goal while adapting the plan based on real-time observations. Recently, prompting Large Language Models (LLMs) to…
Ruikai Liu, Guangxi Wan, Maowei Jiang, Haojie Chen + 2 more
'Ming Xie'] The Agile Robotics for Industrial Automation Competition (ARIAC) was established to advance flexible manufacturing, aiming to increase the agility of robotic assembly systems in unstructured and dynamic industrial environments. ARIAC 2023 introduced eight agility challenges involving faulty parts, flipped…
Yaniel Carreno, Yvan Pétillot, Ronald P. A. Petrick
In real-world applications, the ability to reason about incomplete knowledge, sensing, temporal notions, and numeric constraints is vital. While several AI planners are capable of dealing with some of these requirements, they are mostly limited to problems with specific types of constraints. This paper presents a new…
Marco Aiello, Ilche Georgievski
These are notes for lectures presented at the University of Stuttgart that provide an introduction to key concepts and techniques in AI Planning. Artificial Intelligence Planning, also known as Automated Planning, emerged somewhere in 1966 from the need to give autonomy to a wheeled robot. Since then, it has evolved…
Runquan Gui, Zhihai Wang, Jie Wang, Chi Ma + 6 more
'Mingxuan Yuan' 'Jianye Hao' 'Defu Lian' 'Enhong Chen' 'Feng Wu'] Recent advancements have significantly enhanced the performance of large language models (LLMs) in tackling complex reasoning tasks, achieving notable success in domains like mathematical and logical reasoning. However, these methods encounter challenges…
Moritz J. F. Krusche, Eric Schulz, Arthur Guez, Maarten Speekenbrink
How do people plan ahead when searching for rewards? We investigate planning in a foraging task in which participants search for rewards on an infinite two-dimensional grid. Our results show that their search is best-described by a model which searches approximately 3 steps ahead. Furthermore, participants do not seem…
Yoav Gottlieb, Tal Shima, Felipe Gonzalez Toro
The intertwined task assignment and motion planning problem of assigning a team of fixed-winged unmanned aerial vehicles to a set of prioritized targets in an environment with obstacles is addressed. It is assumed that the targets’ locations and initial priorities are determined using a network of unattended ground…
Josh Zapf, Marco Roveri, Francisco Martín, Juan Carlos Manzanares
'Juan Carlos Manzanares'] Abstract— Executing temporal plans in the real and open world requires adapting to uncertainty both in the environment and in the plan actions. A plan executor must therefore be flexible to dispatch actions based on the actual execution conditions. In general, this involves considering both…
Fatmaalzahraa A Aboalasaad, Leonie Lambertz, Chiara Mastrogiuseppe, Rubén Moreno-Bote
In our everyday lives, we continually need to commit to courses of future action even when direct feedback is not available. The expected reward of an action depends not only on a single decision but on a sequence of interdependent choices that will happen in the future. Admittedly, most decisions we make concern…
Changmin Baek, Kyunghoon Cho, Luis Payá, Oscar Reinoso García + 1 more
'Helder Jesus Araújo'] The presented paper introduces a novel path planning algorithm designed for generating low-cost trajectories that fulfill mission requirements expressed in Linear Temporal Logic (LTL). The proposed algorithm is particularly effective in environments where cost functions encompass the entire…
Abdelaziz Shaarawy, Cansu Erdogan, Rustam Stolkin, Alireza Rastegarpanah
This paper addresses the problem of multi-robot coordination for complex manipulation task sequences. We present a vision-driven task-and-motion planning (TAMP) framework for a real dual-agent platform that integrates task decomposition and allocation with a learning-based planner. A GMM-informed RRT motion planner is…
Mattia Eluchans, Gian Luca Lancia, Antonella Maselli, Marco D’Alessando + 2 more
We humans are capable of solving challenging planning problems, but the range of adaptive strategies that we use to address them are not yet fully characterized. Here, we designed a series of problem-solving tasks that require planning at different depths. After systematically comparing the performance of participants…
Jonatan Scharff Willners, Daniel Gonzalez-Adell, Juan David Hernández, Èric Pairet + 2 more
'Èric Pairet' 'Yvan Petillot' 'Claudio Rossi'] In this paper we present an extension to the hybrid A (HA) path planner. This extension allows autonomous underwater vehicle (AUVs) to plan paths in 3-dimensional (3D) environments. The proposed approach enables the robot to operate in a safe manner by accounting for the…
Juqi Wei, Hai Wang
Autonomous inspection of discrete obstacles (e.g., tree trunks in orchards and forests) requires UAVs to visit every target with proper observation distance and heading, while simultaneously exploring the unknown environment. Existing space-guided exploration methods focus on eliminating unknown space and are…
Authors not listed
Computer-aided synthesis planning aims to identify viable synthetic routes from a target compound to readily available building blocks by iteratively decomposing molecules into smaller precursors. Self-play search algorithms, trained with simulated experience, reach state-of-the-art performance. However, these methods…
Sophia-Helen Sass, Lorenz Gönner, Sarah Schwöbel, Sascha Frölich + 4 more
'Franka Glöckner' 'Stefan J. Kiebel' 'Shu-Chen Li' 'Michael N. Smolka'] When planning an action sequence, it has been shown that humans prune decision trees to reduce computational complexity, instead of considering all possible options. However, little is understood about pruning employed in probabilistic…
Eva-Maria Griesbauer, Pablo Fernandez Velasco, Antoine Coutrot, Jan M. Wiener + 4 more
Humans show an impressive ability to plan over complex situations and environments. A classic approach to explaining such planning has been tree-search algorithms which search through alternative state sequences for the most efficient path through states. However, this approach fails when the number of states is large…
Grzegorz Skoraczyński, Mateusz Kitlas, Błażej Miasojedow, Anna Gambin
Modern computer-assisted synthesis planning tools provide strong support for this problem. However, they are still limited by computational complexity. This limitation may be overcome by scoring the synthetic accessibility as a pre-retrosynthesis heuristic. A wide range of machine learning scoring approaches is…
Authors not listed
Computer-Assisted Synthesis Programs are increasingly employed by organic chemists. Often, these tools combine neural networks for policy prediction with heuristic search algorithms. We propose two novel enhancements, which we call eUCT and dUCT, to the Monte Carlo tree search (MCTS) algorithm. The enhancements were…
Ramith Hettiarachchi, Avi Swartz, Sergey Ovchinnikov
Inferring the most probable evolutionary tree given leaf nodes is an important problem in computational biology that reveals the evolutionary relationships between species. Due to the exponential growth of possible tree topologies, finding the best tree in polynomial time becomes computationally infeasible. In this…
Amol Thakkar, Thierry Kogej, Jean-Louis Reymond, Ola Engkvist + 1 more
Computer Assisted Synthesis Planning (CASP) has gained considerable interest as of late. Herein we investigate a template-based retrosynthetic planning tool, trained on a variety of datasets consisting of up to 17.5 million reactions. We demonstrate that models trained on datasets such as internal Electronic Laboratory…
Authors not listed
We present RetroSynFormer, a novel approach to multi-step retrosynthesis planning. Here, we express the task of iteratively breaking down a compound into building blocks as a sequence-modeling problem and train a model based on the Decision Transformer. The synthesis routes are generated by iteratively predicting…