Language-Conditioned Imitation Learning for Robot Manipulation Tasks
Authors: Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Stefan Lee, Chitta Baral, Heni Ben Amor
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated our model in a dynamic-enabled simulator with random assortments of objects and procedurally generated instructions, with success in 84% of sequential tasks that required picking up a cup and pouring its contents into another vessel. |
| Researcher Affiliation | Collaboration | Simon Stepputtis 1 Joseph Campbell1 Mariano Phielipp2 Stefan Lee3 Chitta Baral1 Heni Ben Amor1 1Arizona State University, 2Intel AI Labs, 3Oregon State University |
| Pseudocode | No | The paper describes the methods in text and uses equations but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All data used in this paper, along with a trained model and the full source code can be found at: https://github.com/ir-lab/Language Policies. |
| Open Datasets | Yes | All data used in this paper, along with a trained model and the full source code can be found at: https://github.com/ir-lab/Language Policies. The final data set contained 22,500 complete task demonstrations composed of the two subtasks (grasping and pouring), resulting in 45,000 training samples. |
| Dataset Splits | Yes | Of these samples, we used 4,000 for validation and 1,000 for testing, leaving 40,000 for training. |
| Hardware Specification | No | The paper does not explicitly specify the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like Faster R-CNN, ResNet-101, GloVe, and Coppelia Sim but does not specify their version numbers or other ancillary software dependencies with versions. |
| Experiment Setup | Yes | The overall loss was a weighted sum of five auxiliary losses: L = αa La + αt Lt + αφLφ + αw Lw + α L . Values αa = 1, αt = 5, αφ = 1, αw = 50, α = 14 were empirically chosen as hyper-parameters for L that had been found by a grid-search approach. We trained our model in a supervised fashion by minimizing L with an Adam optimizer using a learning rate of 0.0001. |