PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception
Authors: Aviv Netanyahu, Tianmin Shu, Boris Katz, Andrei Barbu, Joshua B. Tenenbaum845-853
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | PHASE is validated with human experiments demonstrating that humans perceive rich interactions in the social events, and that the simulated agents behave similarly to humans. As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE (SIMulation, Planning and Local Estimation), which outperforms state-of-the-art feedforward neural networks. We conduct two human experiments to evaluate the quality of this dataset. We propose two machine social perception tasks on this dataset. We test state-of-the-art methods based on feed-forward neural networks and show that they fail to understand or predict many of these social interactions. Table 1 and Table 2 summarize the performance of all methods in the two tasks. |
| Researcher Affiliation | Academia | Aviv Netanyahu*, Tianmin Shu*, Boris Katz, Andrei Barbu, Joshua B. Tenenbaum Massachusetts Institute of Technology, Cambridge, MA 02139 {avivn, tshu, boris, abarbu, jbt}@mit.edu |
| Pseudocode | No | The paper describes the Hierarchical Planner and Bayesian Inverse Planning in prose and through equations, but does not include structured pseudocode blocks or formally labeled algorithm sections. |
| Open Source Code | No | The paper states: 'The dataset and the supplementary material are available at https: //www.tshu.io/PHASE.' While supplementary material often includes code, the statement does not unambiguously confirm the availability of the *source code for the methodology* (e.g., SIMPLE or the simulation engine) as a direct release. |
| Open Datasets | Yes | In this work, we create a dataset of physically-grounded abstract social events, PHASE. The dataset and the supplementary material are available at https: //www.tshu.io/PHASE. |
| Dataset Splits | Yes | PHASE consists of 500 video animations depicting diverse social interactions. With these 500 videos, we create a training set of 320 videos, a validation set of 80 videos, and a testing set of 100 videos. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments, such as CPU or GPU models, memory, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions frameworks and algorithms like Dec-POMDP, A*, POMCP, 2-Layer MLP, 2-Level LSTM, ARG, Social-LSTM, and STGAT. However, it does not provide specific version numbers for any software libraries, tools, or environments used to implement these or conduct the experiments. |
| Experiment Setup | Yes | For the first task, joint goal and relation inference, we compare our model, SIMPLE (with 15 particles and 6 iterations)... We sample a time interval with a fixed length, T, based on the errors between the simulation and the observations, i.e., tl,m e η Ptl,m+ T τ=tl,m ||ˆsτ l,m sτ ||2, where η = 0.1. |