Push Grasp Efficientnet-B0 Test Results v0.3

Grasping Results, release v0.3.

Testing iteration: 1223
Change detected: False (value: 1)
Trainer.get_label_value(): Current reward: 0.000000 Future reward: 0.000000 Expected reward: 0.000000 + 0.500000 x 0.000000 = 0.000000
Primitive confidence scores: 1.462715 (push), 2.052955 (grasp)
Strategy: exploit (exploration probability: 0.000000)
Action: grasp at (13, 67, 148)
Training loss: 0.795837
Executing: grasp at (-0.428000, -0.090000, 0.001002)
gripper position: 0.03202284872531891
gripper position: 0.026405101642012596
gripper position: 0.0013546496629714966
gripper position: -0.021843165159225464
gripper position: -0.021720416843891144
gripper position: -0.021846182644367218
Grasp successful: True
Grasp Count: 1138, grasp success rate: 0.8717047451669596
Time elapsed: 6.122154
Trainer iteration: 1224.000000

Train Command:

export CUDA_VISIBLE_DEVICES="0" && python3 main.py --is_sim --obj_mesh_dir 'objects/toys' --num_obj 10  --push_rewards --experience_replay --explore_rate_decay

Test Command:

 export CUDA_VISIBLE_DEVICES="0" && python3 main.py --is_sim --obj_mesh_dir 'objects/toys' --num_obj 10  --push_rewards --experience_replay --explore_rate_decay --load_snapshot --snapshot_file '/home/costar/src/costar_visual_stacking/logs/2019-08-17.20:54:32-train-grasp-place-split-efficientnet-21k-acc-0.80/models/snapshot.reinforcement.pth' --random_seed 1238 --is_testing --save_visualizations