script.sh

# robosuite tasks
python scripts/run_control.py --multirun hydra.job.name=rs_case_study model=ivideogpt model_name=rs5k_full_ivideogpt planning_modalities=[rgb] agent/optimizer/objective=mse_rgb seed=1,2,3,4 agent.optimizer.log_every=5 sweep=single_task_epoch model.pretrained_transformer_path=/dev/null/iVideoGPT/pretrained_models/vp2_robosuite/tokenizer model.pretrained_vqgan_name_or_path=/dev/null/iVideoGPT/pretrained_models/vp2_robosuite/transformer model.config_name=/dev/null/iVideoGPT/configs/llama/config.json

# robodesk tasks
python scripts/run_control.py --multirun hydra.job.name=rd_case_study planning_modalities=[rgb] seed=1,2,3,4 agent.replan_interval=1 env=robodesk sweep=multi_task_epoch agent.optimizer.init_std=[0.5,0.5,0.5,0.1,0.1] env.task=push_red model=ivideogpt model_name=rdall_full_ivideogpt agent.optimizer.objective.objectives.rgb.weight=0.5 agent.optimizer.objective.objectives.classifier.weight=10 agent/optimizer/objective=combined_classifier_mse agent.optimizer.log_every=5 model.pretrained_transformer_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/tokenizer model.pretrained_vqgan_name_or_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/transformer model.config_name=/dev/null/iVideoGPT/configs/llama/config.json
python scripts/run_control.py --multirun hydra.job.name=rd_case_study planning_modalities=[rgb] seed=1,2,3,4 agent.replan_interval=1 env=robodesk sweep=multi_task_epoch agent.optimizer.init_std=[0.5,0.5,0.5,0.1,0.1] env.task=push_blue model=ivideogpt model_name=rdall_full_ivideogpt agent.optimizer.objective.objectives.rgb.weight=0.5 agent.optimizer.objective.objectives.classifier.weight=10 agent/optimizer/objective=combined_classifier_mse agent.optimizer.log_every=5 model.pretrained_transformer_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/tokenizer model.pretrained_vqgan_name_or_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/transformer model.config_name=/dev/null/iVideoGPT/configs/llama/config.json
python scripts/run_control.py --multirun hydra.job.name=rd_case_study planning_modalities=[rgb] seed=1,2,3,4 agent.replan_interval=1 env=robodesk sweep=multi_task_epoch agent.optimizer.init_std=[0.5,0.5,0.5,0.1,0.1] env.task=push_green model=ivideogpt model_name=rdall_full_ivideogpt agent.optimizer.objective.objectives.rgb.weight=0.5 agent.optimizer.objective.objectives.classifier.weight=10 agent/optimizer/objective=combined_classifier_mse agent.optimizer.log_every=5 model.pretrained_transformer_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/tokenizer model.pretrained_vqgan_name_or_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/transformer model.config_name=/dev/null/iVideoGPT/configs/llama/config.json
python scripts/run_control.py --multirun hydra.job.name=rd_case_study planning_modalities=[rgb] seed=1,2,3,4 agent.replan_interval=1 env=robodesk sweep=multi_task_epoch agent.optimizer.init_std=[0.5,0.5,0.5,0.1,0.1] env.task=upright_block_off_table model=ivideogpt model_name=rdall_full_ivideogpt agent.optimizer.objective.objectives.rgb.weight=0.5 agent.optimizer.objective.objectives.classifier.weight=10 agent/optimizer/objective=combined_classifier_mse agent.optimizer.log_every=5 model.pretrained_transformer_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/tokenizer model.pretrained_vqgan_name_or_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/transformer model.config_name=/dev/null/iVideoGPT/configs/llama/config.json
python scripts/run_control.py --multirun hydra.job.name=rd_case_study planning_modalities=[rgb] seed=1,2,3,4 agent.replan_interval=1 env=robodesk sweep=multi_task_epoch agent.optimizer.init_std=[0.5,0.5,0.5,0.1,0.1] env.task=flat_block_off_table model=ivideogpt model_name=rdall_full_ivideogpt agent.optimizer.objective.objectives.rgb.weight=0.5 agent.optimizer.objective.objectives.classifier.weight=10 agent/optimizer/objective=combined_classifier_mse agent.optimizer.log_every=5 model.pretrained_transformer_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/tokenizer model.pretrained_vqgan_name_or_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/transformer model.config_name=/dev/null/iVideoGPT/configs/llama/config.json
python scripts/run_control.py --multirun hydra.job.name=rd_case_study planning_modalities=[rgb] seed=1,2,3,4 agent.replan_interval=1 env=robodesk sweep=multi_task_epoch agent.optimizer.init_std=[0.5,0.5,0.5,0.1,0.1] env.task=open_slide model=ivideogpt model_name=rdall_full_ivideogpt agent.optimizer.objective.objectives.rgb.weight=0.5 agent.optimizer.objective.objectives.classifier.weight=10 agent/optimizer/objective=combined_classifier_mse agent.optimizer.log_every=5 agent.optimizer.num_samples=800 model.pretrained_transformer_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/tokenizer model.pretrained_vqgan_name_or_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/transformer model.config_name=/dev/null/iVideoGPT/configs/llama/config.json
python scripts/run_control.py --multirun hydra.job.name=rd_case_study planning_modalities=[rgb] seed=1,2,3,4 agent.replan_interval=1 env=robodesk sweep=multi_task_epoch agent.optimizer.init_std=[0.5,0.5,0.5,0.1,0.1] env.task=open_drawer model=ivideogpt model_name=rdall_full_ivideogpt agent.optimizer.objective.objectives.rgb.weight=0.5 agent.optimizer.objective.objectives.classifier.weight=10 agent/optimizer/objective=combined_classifier_mse agent.optimizer.log_every=5 agent.optimizer.num_samples=800 model.pretrained_transformer_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/tokenizer model.pretrained_vqgan_name_or_path=/dev/null/iVideoGPT/pretrained_models/vp2_robodesk/transformer model.config_name=/dev/null/iVideoGPT/configs/llama/config.json