SORNet: Spatial Object-Centric Representations for Sequential Manipulation

[paper] [website] [clevr data] [leonardo data] [pretrained models]

Introduction

SORNet (Spatial Object-Centric Representation Network) is a framework for extracting object-centric representations from RGB observations conditioned on canonical views of the objects of interest. The object embeddings learned by SORNet generalize zero-shot to unseen object entities on spatial reasoning tasks such as spatial relation prediction, skill precondition classification and relative direction regression, significantly outperforming baselines. This repository contains a basic PyTorch implementation for training and evaluating SORNet. Please refer to our project website or our publication at CORL 2021 for more details.

If you find our work useful, please consider citing our paper:

@inproceedings{yuan2021sornet,
    title        = {SORNet: Spatial Object-Centric Representations for Sequential Manipulation},
    author       = {Wentao Yuan and Chris Paxton and Karthik Desingh and Dieter Fox},
    booktitle    = {5th Annual Conference on Robot Learning},
    pages        = {148--157},
    year         = {2021}
    organization = {PMLR}
}

Setup

• Install dependencies by conda env create -f environment.yml.
• Activate virtual environment by conda activate sornet.
• Download data (clevr and leonardo) and copy or sym link to data/.
• Download pre-trained models and copy or sym link to models/.
• Example environment specs (doesn't have to match exactly to run the models):
  • Ubuntu 18.04
  • Pytorch 1.10.0
  • CUDA 11.3

Experiments

Spatial Relation Prediction

In this experiment, we ask SORNet to predict spatial relations (left, right, front, behind) between each pair of objects in a scene with up to 10 objects. We use the CLEVR-CoGenT dataset, where the training and evaluation data contain different sets of objects. The model has not seen any annotation from the evaluation data.

• To evaluate accuracy of the pretrained model:

  python test_clevr.py \
      --data data/clevr_cogent \
      --split valB \
      --checkpoint models/clevr_cogent.pth
  

  • Use the --split flag to switch between valA (same set of objects as training) and valB (different set of objects from training).

• To visualize the prediction:

  python visualize_clevr.py 0 left large_red_rubber_cube small_green_metal_sphere \
      --data data/clevr_cogent \
      --split valB \
      --checkpoint models/clevr_cogent.pth
  

  • The command syntax is python visualize_clevr.py [frame_index] [relation] [object1] [object2].
  • To see the list of available objects, run the following inside a python interactive shell:

    import h5py             
    for obj in h5py.File('data/clevr_cogent/objects.h5'):
        print(obj)
    

• To train a new model:

  python train_clevr.py --data data/clevr_cogent --log log/clevr
  

  • Multi-GPU training via PyTorch DDP is supported. Use --n_gpu to specify the number of GPUs to use for training.
  • Run tensorboard --logdir log/clevr to monitor training progress.
  • Use --resume [checkpoint] to resume training from a checkpoint.

Skill Precondition Classification

In this experiment, we ask SORNet to classify preconditions/predicates of a primitive manipulation skill given an RGB image of a manipulation scene. For example, SORNet needs to determine whether the robot has the red block in its hand, i.e. whether the predicate has_obj(robot, red_block) is true, before the robot executes the skill lift(red_block).

The pretrained model has not seen any of the test objects. It can also generalize to scenes with different number of objects.

• To evaluate accuracy of the pretrained model:

  python test_leonardo.py \
      --data_dir data/leonardo \
      --split test_4obj \
      --obj_file test_objects.h5 \
      --n_views 3 \
      --checkpoint models/leonardo_3view.pth
  

  • Use the --split flag to switch among test sets including 4 (test_4obj), 5 (test_5obj) and 6 (test_6obj) objects.
  • Use the --n_views flag to select the number of views (1 or 3) used for evaluation.
  • Pass the --gripper flag when evaluating the model taking gripper state as input (e.g. models/leonardo_gripper_3view.pth).

• To visualize the prediction

  python visualize_leonardo.py \
      --data data/leonardo \
      --split test_4obj \
      --obj test_objects.h5 \
      --checkpoint models/leonardo_3view.pth
  

  • Use the --split flag to switch among test sets (test_4obj|test_5obj|test_6obj).
  • Use --seq_id and --frame_id to choose the sequence and frame to visualize.
  • In the visualization, black predicates are true positives, blue are false positives and red are false negatives.

• To visualize learned attention:

  python visualize_attention.py \
      --data leonardo \
      --split test_4obj \
      --obj test_objects.h5 \
      --checkpoint models/leonardo_3view.pth
  

  • Use the --split flag to switch among test sets (test_4obj|test_5obj|test_6obj).
  • Use --seq_id and --frame_id to choose the sequence and frame to visualize.
  • Use --layer_to_vis to chosse the layer for which the attention weights are visualized.
  • In the figure, the context patches are color coded by the attention weights from the query patches. Darker indicates more attention.

• To train a new model:

  python train_leonardo.py --data data/leonardo --log log/leonardo
  

  • Use the --n_views flag to select the number of views (1 or 3) used to train the model.
  • Pass the --gripper flag to train model taking gripper state as input.
  • Multi-GPU training via PyTorch DDP is supported. Use --n_gpu to specify the number of GPUs to use for training.
  • Run tensorboard --logdir log/leonardo to monitor training progress.
  • Use --resume [checkpoint] to resume training from a checkpoint.

Relative Direction Regression

In this experiment, we train readout networks to regress continuous 3D directions among entities (e.g. the end effector and objects) on top of a frozen embedding network trained on predicate classificaiton. This experiment shows that the object embedding learned by SORNet contains continuous spatial information even though it is trained with no metric supervision.

• To evaluate accuracy of the pretrained model:

  python test_regression.py \
      --data data/leonardo \
      --split test_4obj \
      --obj_file test_objects.h5 \
      --n_views 3 \
      --model models/leonardo_3view.pth \
      --head_checkpoint models/leonardo_regression_obj-obj_dir_3view.pth
  

  • Use the --dist flag to test models trained to regress distance instead of unit xyz direction (e.g. models/leonardo_regression_obj-obj_dist_3view.pth).
  • Use the --ee flag to test models trained to regress quatinties from end effector to objects instead of between pairs of objects (e.g. models/leonardo_regression_ee-obj_dir_3view.pth).

• To visualize the prediction:

  python visualize_regression.py \
      --data data/leonardo \
      --split test_4obj \
      --obj test_objects.h5 \
      --model models/leonardo_3view.pth \
      --dir_head models/leonardo_regression_obj-obj_dir_3view.pth \
      --dist_head models/leonardo_regression_obj-obj_dist_3view.pth
  

  • Use the --split flag to switch among test sets (test_4obj|test_5obj|test_6obj).
  • Use --seq_id and --frame_id to choose the sequence and frame to visualize.
  • Use --ee to visualize models that regress quatinties from end effector to objects.
  • In the figure, the arrows are color coded by the target object and the length is scaled by the predicted distance.

• To train a new model:

  python train_leonardo.py \
      --data data/leonardo \
      --log log/regression/obj-obj_dir \
      --model models/leonardo_3view.pth
  

  • Use --model [checkpoint] to specify weights for the pretrained embedding network.
  • Use --ee to train models that regress quatinties from end effector to objects.
  • Use --dist to train models that regress distance.
  • Multi-GPU training via PyTorch DDP is supported. Use --n_gpu to specify the number of GPUs to use for training.
  • Run tensorboard --logdir log/regression/obj-obj_dir to monitor training progress.