Dynamically Constructed (PO)MDPs for Adaptive Robot Planning
Authors: Shiqi Zhang, Piyush Khandelwal, Peter Stone
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 s.zhang9@csuohio.edu; {piyushk,pstone}@cs.utexas.edu |
| 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. |