Efficient Optimization for Autonomous Robotic Manipulation of Natural Objects
Authors: Abdeslam Boularias, James Bagnell, Anthony Stentz
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This approach is tested on the task of clearing piles of real, unknown, rock debris with an autonomous robot. Empirical results show a clear advantage of the proposed approach when the time window for decision is short. |
| Researcher Affiliation | Academia | Abdeslam Boularias and J. Andrew Bagnell and Anthony Stentz The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA {Abdeslam, dbagnell, tony}@andrew.cmu.edu |
| Pseudocode | No | The paper includes a pipeline overview diagram (Figure 2) and mathematical equations, but it does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the methodology described. |
| Open Datasets | No | The paper describes collecting examples from "depth images of 16 piles of man-made and natural objects" and testing on "five piles of unknown objects", indicating custom data collection. However, it does not provide concrete access information (link, DOI, or formal citation with authors/year) for these datasets, nor does it specify using a well-known public dataset with access details. |
| Dataset Splits | No | The paper describes testing on different piles of objects and averaging results over multiple starting points, but it does not provide specific percentages or counts for training, validation, and test dataset splits, nor does it refer to predefined standard splits for reproducibility. |
| Hardware Specification | No | The paper states that "All the results reported here were obtained by using a single-core CPU implementation." This is too vague and does not provide specific hardware details like CPU model, speed, or memory. |
| Software Dependencies | No | The paper mentions using "Point Cloud Library (PCL)" and the "CHOMP algorithm" but does not provide specific version numbers for these software components. "To simulate the trajectories of the robotic hand and fingers, we implemented a 3-D model of the Barrett hand using the Point Cloud Library (PCL) (Rusu and Cousins 2011)." and "The CHOMP algorithm (Ratliff et al. 2009) is then used to generate arm trajectories" |
| Experiment Setup | Yes | We consider 100 regular rotations from 0 to 2π. In the first experiments, we limit the time of the perception module to only one second per executed action. In the second experiments, we increase the time budget to five seconds. |