Learning to Act for Perceiving in Partially Unknown Environments
Authors: Leonardo Lamanna, Mohamadreza Faridghasemnia, Alfonso Gerevini, Alessandro Saetti, Alessandro Saffiotti, Luciano Serafini, Paolo Traverso
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate the proposed approach on several synthetic datasets, and show the feasibility of our approach in a real-world scenario that involves noisy perceptions and noisy actions on a real robot. |
| Researcher Affiliation | Academia | 1Fondazione Bruno Kessler, Trento, Italy 2Center for Applied Autonomous Sensor Systems, University of Orebro, Sweden 3Department of Information Engineering, University of Brescia, Italy |
| Pseudocode | Yes | Algorithm 1 FIND BELIEF STATES |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of its source code. |
| Open Datasets | Yes | Cifar10, Cifar100, Euro SAT, FER, MNIST, Oxford Pet |
| Dataset Splits | No | Table 1 provides '#Train' and '#Test' set sizes but no explicit 'Validation' set split information. The paper generally refers to training on the 'noisy training set' and evaluating on the 'noisy test set'. |
| Hardware Specification | No | The paper mentions 'a Softbank Robotic s Pepper humanoid robot' for real-world experiments, but does not provide specific hardware specifications (e.g., CPU, GPU models, or memory) for the computational resources used for training or conducting the main experiments. |
| Software Dependencies | No | The paper mentions general software components like 'Res Net' and 'planner Fast Downward' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | All images have been modified by: (i) decreasing the brightness by 90% with a 0.5 probability; (ii) blurring the image by 50% with a 0.5 probability; and (iii) adding an occluding circle centered in a position uniformly sampled from the image size and with a diameter equal to 70% of the image size. Our approach (with a confidence threshold t = 0.9)... K-means algorithm with K = 8. The viewpoints where the property is observable are obtained by selecting the clusters with an average confidence higher than a threshold t = 0.8. |