Making Robots Proactive through Equilibrium Maintenance

Authors: Jasmin Grosinger, Federico Pecora, Alessandro Saffiotti

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We provide formal evidence that equilibrium maintenance is conducive to proactive robots, and demonstrate our approach in a closed loop with a real robot in a smart home.In this section we present an example scenario implemented in a domestic service robotic system.The results (opportunities, action scheme selection and execution) are summarized in Table 1, where the state is characterized by its positive predicates.
Researcher Affiliation Academia Jasmin Grosinger, Federico Pecora, Alessandro Saffiotti Center for Applied Autonomous Systems (AASS), Orebro University, 70182 Orebro, Sweden, {jngr,fpa,asaffio}@aass.oru.se
Pseudocode Yes Algorithm 1 describes the Eq M(K) procedure which realizes the Equilibrium Maintenance loop.
Open Source Code No The paper mentions using 'the open-source planning system JSHOP [Nau et al., 2003]' but does not provide open-source code for the methodology described in this paper.
Open Datasets No The paper describes a specific scenario implemented in a domestic service robotic system and smart home with custom sensor setups, not a publicly available dataset with concrete access information.
Dataset Splits No The paper describes a robot demonstration scenario and does not specify dataset splits (e.g., train/validation/test percentages or counts) typically found in data-driven experiments.
Hardware Specification Yes The robot, a Scitos G5 with a Kinova Jaco arm, moves freely in a smart home with Xbee pressure sensors mounted under chairs (see Figure 3).
Software Dependencies No The paper mentions using 'the open-source planning system JSHOP [Nau et al., 2003]' but does not provide specific version numbers for JSHOP or any other software dependencies.
Experiment Setup Yes The free-run model is captured by the transitions f(morning, ?, noon), f(noon, ?, evening) and f(evening, ?, night), together with the user model encoded by the following rules: morning ) pillstaken kitchen noon ) lunch night ) sleeping night pillstaken ) well.The time horizon used to compute Eq(s, K) in Eq M(K) is K = 1.