Fast Motion Prediction for Collaborative Robotics
Authors: Claudia Pérez-D’Arpino, Julie A. Shah
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithm was validated off-line using real human motion data and tested on-line using a cooperative table-top manipulation task with a PR2 robot. Our findings show considerable improvement over current techniques in early prediction, with the algorithm achieving 70% or higher average correct classification having observed the first third of the trajectory ( 400msec), enabling on-line prediction in a timely fashion [P erez-D Arpino and Shah., 2015]. When compared to a GMM approach, we observed that our method improved classification accuracy by 15% at most in the early part of the trajectory, when the number of Gaussian components was limited to run in real time, comparable to our method. |
| Researcher Affiliation | Academia | Claudia P erez-D Arpino and Julie A. Shah Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology {cdarpino, julie a shah} @csail.mit.edu |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | No | The paper mentions a video link: 'video available at https://youtu.be/OT3yb T-e6l0', but no link or statement about open-source code for the methodology itself. |
| Open Datasets | No | The paper describes the collection of 'real human motion data' for their task, stating 'This task required the collection of parts from a table, with four possible targets located along a single axis and three possible initial positions, for a total of 12 possible motion classes. Fig.2a depicts the 3D trajectories of the right hand of the human operator performing 20 demonstrations of each motion class.' However, no link, DOI, repository name, or formal citation for public access to this dataset is provided. |
| Dataset Splits | Yes | Validation was performed using 25 random libraries of motions per training set to record the percentage of correct classification per time step across all 12 motion classes using five random test trajectories, for a total of 125 tests per class. |
| Hardware Specification | No | The paper mentions using 'a PR2 robot' for the collaborative task but provides no specific details about the computing hardware (e.g., GPU, CPU models, or cloud instances) used to run the algorithm or experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming language versions, library versions, or solver versions). |
| Experiment Setup | No | The paper describes the experimental task setup (e.g., '12 possible motion classes') and aspects of validation, but it does not provide specific experimental setup details such as hyperparameters (learning rate, batch size, etc.), optimizer settings, or model initialization details. |