concept_learning

Downloading Object Assets and Pre-trained Models:

Download data.tar.gz and place it under the data folder. Unzip it with the following command:

tar zxvf data.tar.gz

Functionality:

1. a) Generate passive data using Isaac Gym:

   python datasets/generate_concept_data.py --headless --cuda --envs <num_envs> --samples <num_samples>

Options:

• --headless: starts data generation without simulator visualizer;
• --cuda: activates GPU computation;
• <num_envs>: number of Gym environments to generate data in parallel;
• <num_samples>: number of samples to collect per environment.

1. b) Generate active data:

   python datasets/collect_human_data.py --config <config_path> --concept <concept> --simulated --active_samples <active_samples> --passive_samples <passive_samples> --batch_size <batch_size> --objective <objective> --warmstart <warmstart_samples> --mining <mining_samples>

Options:

• <concept>: concept to ask for queries about ("above180", "above45", "abovebb", "near", "upright", "upright45", "alignedhorizontal", "alignedvertical", "front", "front45", "ontop");
• <config_path>: path to the /configs folder configuration file that specifies training parameters;
• --simulated: uses the oracle human to label the queries, otherwise uses real human input via interface;
• <active_samples>: number of samples to collect from the human actively (label queries);
• <passive_samples>: number of samples to collect from the human passively (demo queries);
• <batch_size>: how often to retrain the active learning model;
• <objective>: active learning objective ("random", "min", "max", "confusion", "allrand", "confrand")
• --warmstart: number of demonstration queries to use for warmstarting the model;
• --mining: number of queries that use mining.

2. Label data using ground truth or learned concept:

   python datasets/label_concept_data.py --concept <concept> --concept_model <concept_model>

Options:

• <concept>: name of desired concept ("above180", "above45", "abovebb", "near", "upright", "upright45", "alignedhorizontal", "alignedvertical", "front", "front45", "ontop");
• <concept_model>: name of ".pt" model weights file.

3. Create test-train split files for oracle demonstration data:

   python datasets/split_data.py --concept <concept>

Options:

• <concept>: name of desired concept ("above180", "above45", "abovebb", "near", "upright", "upright45", "alignedhorizontal", "alignedvertical", "front", "front45", "ontop");

4. Train concept:

   python train/train_oracle_concept.py --concept_dir <concept_dir> --config <config_path> --train_amt <train_amount>

Options:

• <concept_dir>: concept's label directory;
• <config_path>: path to the /configs folder configuration file that specifies training parameters;
• <train_amt>: data amount used for training (optional: if left out, uses all data);

5. Evaluate concept:

   python train/evaluate_concept.py --concept_dir <concept_dir> --config <config_path> --concept_model <concept_model>

Options:

• <concept_dir>: concept's label directory;
• <config_path>: path to the /configs folder configuration file that specifies evaluation parameters;
• <concept_model>: model to use for evaluation;

Note: The above functionalities are replicated for ngc job scheduling in the /scripts folder.

Passive Example (Demo Queries):

1. Generate Isaac Gym data: python datasets/generate_concept_data.py --headless --cuda --envs 100 --samples 10000
2. Label data with ground truth: python datasets/label_concept_data.py --concept "above45"
3. Split data in a balanced way (demo queries): python datasets/split_data.py --concept "above45"
4. Train low-dimensional concept: python train/train_oracle_concept.py --concept_dir "above45" --config '/../../configs/rawstate_oracle.yaml' --train_amt 500
5. Train baseline: python train/train_oracle_concept.py --concept_dir "above45" --config '/../../configs/pointcloud_oracle.yaml' --train_amt 500
6. Train handcrafted: python train/train_oracle_concept.py --concept_dir "above45" --config '/../../configs/pointcloud_oracle.yaml'
7. Label using the low-dimensional concept: python datasets/label_concept_data.py --concept "above45" --concept_model 'oracle_rawstate_500_0.pt'
8. Train ours: python train/train_oracle_concept.py --concept_dir above45_500 --config '/../../configs/pointcloud_oracle.yaml'
9. Evaluate model: python train/evaluate_concept.py --concept_dir "above45" --config '/../../configs/pointcloud_oracle.yaml' --concept_model 'oracle_pointcloud_g500_0.pt'

Active Example (Label Queries):

1. Generate active learning data: python datasets/collect_human_data.py --config '/../../configs/rawstate_AL.yaml' --concept "above45" --simulated --active_samples 1000 --passive_samples 0 --batch_size 100 --objective 'confrand' --warmstart 0 --mining 1000
2. Train low-dimensional concept: python train/train_human_concept.py --concept "above45" --config '/../../configs/rawstate_human.yaml' --train_amt 1000 --strategy 'confrandmine'
3. Train baseline: python train/train_human_concept.py --concept "anove45" --config '/../../configs/pointcloud_human.yaml' --train_amt 1000 --strategy 'randomgt'
4. Label using the low-dimensional concept: python datasets/label_concept_data.py --concept "above45" --concept_model 'confrandgt_rawstate_1000_0.pt'
5. Train ours: python train/train_oracle_concept.py --concept_dir above45_confrandgtmine500 --config '/../../configs/pointcloud_oracle.yaml'
6. Evaluate model: python train/evaluate_concept.py --concept_dir "above45" --config '/../../configs/pointcloud_oracle.yaml' --concept_model 'confrandgtmine_pointcloud_g500_0.pt'