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Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics

By Kuo-Hao Zeng, Luca Weihs, Roozbeh Mottaghi, and Ali Farhadi

Paper | Video | BibTex

A common assumption when training embodied agents is that the impact of taking an action is stable; for instance, executing the “move ahead” action will always move the agent forward by a fixed distance, perhaps with some small amount of actuator-induced noise. This assumption is limiting; an agent may encounter settings that dramatically alter the impact of actions: a move ahead action on a wet floor may send the agent twice as far as it expects and using the same action with a broken wheel might transform the expected translation into a rotation. Instead of relying that the impact of an action stably reflects its pre-defined semantic meaning, we propose to model the impact of actions on-the-fly using latent embeddings. By combining these latent action embeddings with a novel, transformer-based, policy head, we design an Action Adaptive Policy (AAP). We evaluate our AAP on two challenging visual navigation tasks in the AI2-THOR and Habitat environments and show that our AAP is highly performant even when faced, at inference-time with missing actions and, previously unseen, perturbed action space. Moreover, we observe significant improvement in robustness against these actions when evaluating in real-world scenarios.

Installation

0. Requirements

   We implement this codebase on Ubuntu 18.04.3 LTS.

   In addition, this codebase needs to be executed on GPU(s).

1. Clone this repository

   git clone git@github.com:KuoHaoZeng/AAP.git
   

2. Intsall xorg if the machine does not have it

   Note: This codebase should be executed on GPU. Thus, we need xserverfor GPU redering.

   # Need sudo permission to install xserver
   sudo apt-get install xorg
   

   Then, do the xserver refiguration for GPU

   sudo python scripts/startx.py
   

3. Using conda, create an environment and install the necessary packages

   Note: The python version we used is 3.8.

   conda create -n aap python=3.8
   conda activate aap
   pip install -r requirements.txt; pip install -r dev_requirements.txt
   pip install -r allenact_plugins/robothor_plugin/extra_requirements.txt
   pip install -r allenact_plugins/clip_plugin/extra_requirements.txt
   

Environment/Dataset

We consider Point Navigation (PointNav) and Object Navigation (ObjNav) tasks in RoboTHOR environment:

To download the data, simply use following commands:

cd datasets
sh download_navigation_datasets.sh robothor-pointnav
sh download_navigation_datasets.sh robothor-objectnav
cd ..c

For more information about how to control an agent in AI2-iTHOR, please vist this webpage.

Pretrained Models

We currently provide the following pretrained models:

These models can be downloaded from the above links and should be placed into the pretrained_model_ckptsdirectory. For example:

cd pretrained_model_ckpts/PointNav
wget https://homes.cs.washington.edu/~khzeng/AAP/PointNav/AAP.pt
cd ../..

You can then, for example, run inference for the NIE model on ObsNav using AllenAct by running:

PYTHONPATH=. python main.py projects/scmb/pointnav/robothor/action_adaptive_policy.py -s 12345 -c pretrained_model_ckpts/PointNav/AAP.pt -o eval_output/PointNav/aap --eval

Train a new model from scratch with AllenAct

We use the AllenAct framework for training the baseline models and our AAP models, the necessary packages for AllenAct are automatically installed when installing the requirements for this project.

Let's say you want to train a AAP model on PointNav task. This can be easily done by running the command

PYTHONPATH=. python main.py projects/scmb/pointnav/robothor/action_adaptive_policy.py -s 12345 -o train_output/PointNav/aap

Citation

If you find this project useful in your research, please consider citing our paper:

@inproceedings{khz2021interact,
  author = {Zeng, Kuo-Hao and Weihs, Luca and Mottaghi, Roozbeh and Farhadi, Ali},
  title = {Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics},
  booktitle = {ICLR},	    
  year = {2023}
}