Entity-Centric Reinforcement Learning for Object Manipulation from Pixels
Authors: Dan Haramati, Tal Daniel, Aviv Tamar
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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 {haramati, taldanielm}@campus.technion.ac.il; avivt@technion.ac.il |
| 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. |