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Found 19 papers, 0 papers with code
Automatic Verbal Depiction of a Brick Assembly for a Robot Instructing Humans
Verbal and nonverbal communication skills are essential for human-robot interaction, in particular when the agents are involved in a shared task.
On Guiding Search in HTN Temporal Planning with non Temporal Heuristics
This is partly due to the lack of a formal and consensual definition of what a temporal hierarchical planning problem is as well as the difficulty to develop heuristics in this context.
HDDL 2.1: Towards Defining a Formalism and a Semantics for Temporal HTN Planning
HDDL, a hierarchical extension of the Planning Domain Definition Language (PDDL), unlike PDDL 2. 1 does not allow to represent planning problems with numerical and temporal constraints, which are essential for real world applications.
iRoPro: An interactive Robot Programming Framework
In fact, teaching robots new actions from scratch that can be reused for previously unseen tasks remains a difficult challenge and is generally left up to robotics experts.
TempAMLSI : Temporal Action Model Learning based on Grammar Induction
In this paper, we present TempAMLSI, an algorithm based on the AMLSI approach able to learn temporal domains.
From Classical to Hierarchical: benchmarks for the HTN Track of the International Planning Competition
All of these benchmarks are based on the HDDL language.
Totally and Partially Ordered Hierarchical Planners in PDDL4J Library
In this paper, we outline the implementation of the TFD (Totally Ordered Fast Downward) and the PFD (Partially ordered Fast Downward) hierarchical planners that participated in the first HTN IPC competition in 2020.
AMLSI: A Novel Accurate Action Model Learning Algorithm
This paper presents new approach based on grammar induction called AMLSI Action Model Learning with State machine Interactions.
Mean-based Heuristic Search for Real-Time Planning
In this paper, we introduce a new heuristic search algorithm based on mean values for real-time planning, called MHSP.
A Review on Learning Planning Action Models for Socio-Communicative HRI
AI techniques, such as machine learning, can be used to learn behavioral models (also known as symbolic action models in AI), intended to be reusable for AI planning, from the aforementioned multimodal traces.
Planification par fusions incrémentales de graphes
The originality of our model consists in considering as soon as possible, {\it i. e.,} in the individual planning process, the positive and the negative interactions between agents activities in order to reduce the search cost of a global coordinated solution plan.
Coordinated exploration for labyrinthine environments with application to the Pursuit-Evasion problem
This paper introduces a multirobot cooperation approach to solve the "pursuit evasion" problem for mobile robots that have omnidirectional vision sensors.
A Framework for Robot Programming in Cobotic Environments: First user experiments
The increasing presence of robots in industries has not gone unnoticed.
Assumption-Based Planning
The purpose of the paper is to introduce a new approach of planning called Assumption-Based Planning.
Hedging Algorithms and Repeated Matrix Games
This paper experimentally shows that well-selected hedging algorithms are better on average than all previous MAL algorithms on the task of playing RMG against various players.
Action Model Acquisition using LSTM
In the field of Automated Planning and Scheduling (APS), intelligent agents by virtue require an action model (blueprints of actions whose interleaved executions effectuates transitions of the system state) in order to plan and solve real world problems.
Handwriting styles: benchmarks and evaluation metrics
It is a key component in order to develop in developing systems with more personalized experiences with humans.
Learning Macro-actions for State-Space Planning
Planning has achieved significant progress in recent years.
