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..
Learning Affordance Landscapes for Interaction Exploration in 3D Environments
Authors: Tushar Nagarajan, Kristen Grauman
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate agents ability to interact with as many objects as possible (Sec. 4.1) and enhance policy learning on downstream tasks (Sec. 4.2). Simulation environment We experiment with AI2-i THOR [30] (see Fig. 1), since it supports context-specific interactions that can change object states, vs. simple physics-based interactions in other 3D indoor environments [59, 8]. The results show agents can learn how to use new home environments intelligently and that it prepares them to rapidly address various downstream tasks like find a knife and put it in the drawer. |
| Researcher Affiliation | Collaboration | Tushar Nagarajan UT Austin and Facebook AI Research EMAIL Kristen Grauman UT Austin and Facebook AI Research EMAIL |
| Pseudocode | No | The paper describes its methods in text and diagrams (e.g., Figure 2), but does not provide a formal pseudocode or algorithm block. |
| Open Source Code | Yes | Project page: http://vision. cs.utexas.edu/projects/interaction-exploration/ |
| Open Datasets | Yes | Simulation environment We experiment with AI2-i THOR [30] (see Fig. 1), since it supports context-specific interactions that can change object states, vs. simple physics-based interactions in other 3D indoor environments [59, 8]. |
| Dataset Splits | Yes | We split the 30 scenes into training (20), validation (5), and testing (5) sets. |
| Hardware Specification | No | The paper mentions 'UT Systems Administration team for their help setting up experiments on the cluster' in the Acknowledgments, indicating experiments were run on a cluster. However, it does not provide specific hardware details such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions using algorithms and architectures such as 'PPO [54]' and 'U-Net [49] architecture'. However, it does not specify any software dependencies or libraries with their corresponding version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | At each time step, we receive the current egocentric frame x and generate its affordance maps ˆy = FA(x). The visual observations and affordance maps are encoded using a 3-layer convolutional neural network (CNN) each, and then concatenated and merged using a fully connected layer. This is then fed to a gated recurrent unit (GRU) recurrent neural network to aggregate observations over time, and finally to an actor-critic network (fully connected layers) to generate the next action distribution and value. We train this network using PPO [54] for 1M frames, with rollouts of T = 256 time steps. |