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Incremental plan aggregation for generating policies in MDPs
TLDR
A way to generate policies in MDPs by determinizing the given MDP model into a classical planning problem, and using sequential Monte-Carlo simulations of the partial policies before execution, in order to assess the probability of replanning for a policy during execution is described. Expand
Two-handed gesture recognition and fusion with speech to command a robot
TLDR
A flexible multimodal interface based on speech and gesture modalities in order to control the authors' mobile robot named Jido is described and a probabilistic and multi-hypothesis interpreter framework is shown to improve the classification rates of multi-modality commands compared to using either modality alone. Expand
Rackham: An Interactive Robot-Guide
TLDR
The focus was to develop and test a methodology to integrate human-robot interaction abilities in a systematic way and to incrementally enhance the robot functional and decisional capabilities based on the observation of the interaction between the public and the robot. Expand
HiPOP: Hierarchical Partial-Order Planning
TLDR
A new planner, HiPOP (Hierarchical Partial-Order Planner), which is domain-configurable and uses POP techniques to create hierarchical time-flexible plans that follows the given methods. Expand
RFF : A Robust , FF-Based MDP Planning Algorithm for Generating Policies with Low Probability of Failure
Over the years, researchers have developed many efficient techniques, such as the planners FF (Hoffmann and Nebel 2001), LPG (Gerevini, Saetti, and Serina 2003), SATPLAN (Kautz, Selman, and HoffmannExpand
Open Loop Execution of Tree-Search Algorithms
TLDR
This work proposes a method for open loop control via a new algorithm taking the decision of re-planning or not at each time step based on an analysis of the statistics of the sub-tree as an action recommender and shows that the probability of selecting a subopti-mal action at any depth of the tree can be upper bounded and converges towards zero. Expand
Robot introspection through learned hidden Markov models
TLDR
It is demonstrated that it is feasible to automate the entire process of learning a high quality HMM from the data recorded by the robot during execution of its task, and the learned HMM can be used both for monitoring and controlling the behaviour of the robot. Expand
Learning the behavior model of a robot
TLDR
A general framework for learning from observation data the behavior model of a robot when performing a given task is proposed and it is shown how such a probabilistic model can be learned and used to improve, on line, the robot behavior with respect to a specific environment and user preferences. Expand
A generic framework for anytime execution-driven planning in robotics
TLDR
This work presents a new generic and anytime planning concept for modular robotic architectures, which manages multiple planning requests at a time, solved in background, while allowing for reactive execution of planned actions at the same time. Expand
Constraint-Based Controller Synthesis in Non-Deterministic and Partially Observable Domains
TLDR
This approach relaxes some restrictive assumptions made by existing work on controller synthesis with non-determinism and partial observability and is shown to induce potentially significant gains. Expand
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