Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Dynamically Constructed (PO)MDPs for Adaptive Robot Planning
Authors: Shiqi Zhang, Piyush Khandelwal, Peter Stone
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a number of experimental trials using two example problems in simulation and demonstrate i CORPP on a real robot. Results show significant improvements compared to competitive baselines. |
| Researcher Affiliation | Academia | Shiqi Zhang,1,2 Piyush Khandelwal,2 Peter Stone2 1 Department of Electrical Engineering and Computer Science, Cleveland State University 2 Department of Computer Science, The University of Texas at Austin EMAIL; EMAIL |
| Pseudocode | Yes | Algorithm 1 specifies the i CORPP algorithm. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper describes tasks within a simulation environment (GAZEBO) and on a real robot, but does not explicitly mention or provide access information for a publicly available or open dataset used for training/evaluation. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper mentions demonstration "on a real robot" and "simulation environment", but does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running experiments. |
| Software Dependencies | No | We used a solver introduced in (Zhu 2012) for P-LOG programs (except that reasoning about reward was manually conducted), the APPL solver for POMDPs (Kurniawati, Hsu, and Lee 2008), and value iteration for MDPs (Sutton and Barto 1998). |
| Experiment Setup | Yes | In the default and cautious versions of i CORPP, the values of [R+,R ] are [20, 20] and [30, 30] respectively. ... We limit the number of random walkers to be 1 and its speed to be one fifth of the robot s. |