Imitation Learning from Visual Data with Multiple Intentions
Authors: Aviv Tamar, Khashayar Rohanimanesh, Yinlam Chow, Chris Vigorito, Ben Goodrich, Michael Kahane, Derik Pridmore
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our approach on learning a reaching task with the IRB-120 robot (Figure 1b)... Table 1 shows the overall success rate for every model across all 200 trials. |
| Researcher Affiliation | Collaboration | 1EECS Department, UC Berkeley 2Osaro Inc. |
| Pseudocode | Yes | Algorithm 1: IDS Input: A minibatch of K samples {ut, xt, . . . , ut+K, xt+K} from the same demonstration trajectory T i Output: An update direction for θ, and a sample from Qα 1 Sample z1, . . . , z N P(z) 2 Set z = arg maxzi P(ut:t+K|xt:t+K, zi; θ) 3 return θ log P(ut:t+K|xt:t+K, z ; θ), and z |
| Open Source Code | No | The paper does not explicitly state that the source code for the methodology is openly available or provide a link to a code repository. It only provides a link to video results. |
| Open Datasets | No | The paper describes a custom collected dataset: 'We collected demonstrations from 468 different task configurations, for a total of 1404 demonstration trajectories.' However, no access information (link, DOI, citation) is provided to make this dataset publicly available. |
| Dataset Splits | Yes | using 90% of the data set for training and 10% for validation. |
| Hardware Specification | No | The paper mentions the 'IRB-120 robot' used for the task, but does not specify the computing hardware (e.g., GPU/CPU models, memory, cloud instances) used for training or running the models. |
| Software Dependencies | No | The paper mentions using 'Adam (Kingma & Ba, 2014)' for optimization, but does not provide specific version numbers for Adam or any other software libraries or frameworks used in the implementation. |
| Experiment Setup | Yes | In all our experiments we used the following parameters for the SNN training... (1) Loss function: ...an L1 regression loss... (2) Monte Carlo samples: we chose N = 5... (3) Intention variable dimension: we chose z R5 for all experiments... (4) P(z): a 5-dimensional vector of independent uniform multinomials in {0 : 4}. |