Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Entity-Centric Reinforcement Learning for Object Manipulation from Pixels
Authors: Dan Haramati, Tal Daniel, Aviv Tamar
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on several simulated tabletop robotic object manipulation environments implemented with Isaac Gym (Makoviychuk et al., 2021). The environment includes a robotic arm set in front of a table with a varying number of cubes in different colors. |
| Researcher Affiliation | Academia | Dan Haramati, Tal Daniel, Aviv Tamar Department of Electrical and Computer Engineering, Technion Israel Institute of Technology EMAIL; EMAIL |
| Pseudocode | No | The paper describes its methods in detail and includes an outline of the architecture (Figure 2), but it does not contain explicit pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | Our code is publicly available on https://github.com/Dan Hrmti/ECRL. |
| Open Datasets | No | We collect 600,000 images from 2 viewpoints by interacting with the environment using a random policy for 300,000 timesteps. |
| Dataset Splits | No | The paper describes collecting data via interaction with a simulated environment and training models, but it does not explicitly provide details on how this collected data is split into training, validation, and test sets with specific percentages or counts. |
| Hardware Specification | No | The paper states that environments are implemented with Isaac Gym and makes general references to GPU-based simulation, but it does not specify concrete hardware details such as GPU models, CPU types, or memory used for experiments. |
| Software Dependencies | No | We implement our RL algorithm with code adapted from stable-baselines3 (Raffin et al., 2021). We train a DLPv2 (Daniel & Tamar, 2023) using the publicly available code base. The paper mentions frameworks and libraries with their publication years but does not provide specific version numbers required for exact replication (e.g., `stable-baselines3==x.y.z`). |
| Experiment Setup | Yes | Further details and hyper-parameters can be found in Appendix D. Our code is publicly available on https://github.com/Dan Hrmti/ECRL. The paper includes detailed tables such as “Table 7: General hyper-parameters used for RL training.” and “Table 9: Hyper-parameters for the EIT architecture.” listing specific values for learning rates, batch sizes, attention dimensions, etc. |