Toward Estimating Others' Transition Models Under Occlusion for Multi-Robot IRL
Authors: Kenneth Bogert, Prashant Doshi
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate m IRL /T +Int in an application domain introduced by Bogert and Doshi [2014] involving mobile robots, I and J, patrolling hallways using cyclical trajectories as shown in Fig. 2. ... We report the success rates of the physical runs in Table 1. |
| Researcher Affiliation | Academia | Kenneth Bogert and Prashant Doshi THINC Lab, Department of Computer Science University of Georgia, Athens, GA 30602 {kbogert,pdoshi}@uga.edu |
| Pseudocode | No | The paper describes mathematical formulations and iterative procedures in prose but does not provide a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing the source code for its methodology or a link to a code repository. |
| Open Datasets | No | The paper describes data generated from simulations and physical robot experiments but does not provide concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper evaluates performance through simulations and physical runs but does not provide specific dataset split information (percentages, sample counts, or detailed splitting methodology) for training, validation, and testing. |
| Hardware Specification | Yes | Each robot in our simulations and physical experiments is a Turtle Bot equipped with a Kinect, which provides a camera and an infrared ranging sensor. The bases include an i Robot Create or a Kobuki. |
| Software Dependencies | Yes | Each robot also has a laptop running ROS Hydro on Ubuntu 12.04. A robot identifies another by detecting its unique color signature using CMVision s blob finder. ROS s default actuator and sensor models for the Turtle Bot and the default local motion planner in move base are used for navigation. Each robot localizes in a predefined map using the adaptive Monte Carlo localization available in ROS. The virtual simulations are performed in Stage. |
| Experiment Setup | Yes | The learner s transition function models the probability of any of its own action failing at 2.5%. ... We experiment with m IRL /T +Int, m IRL +Int fixing a transition success rate of 0.9, and Random in both scenarios: when J s left wheel is artificially damaged thereby slowing it down and creating an uneven trajectory, and when the patrollers are operating properly. |