So I’ve found how how to export the model for the dofbot using this code:
https://github.com/Denys88/rl_games/blob/master/notebooks/train_and_export_onnx_example_continuous.ipynb
so I included in the training script from the Dofbot repo to train the model in rl_games train:
def run(self):
class ModelWrapper(torch.nn.Module):
'''
Main idea is to ignore outputs which we don't need from model
'''
def __init__(self, model):
torch.nn.Module.__init__(self)
self._model = model
def forward(self, input_dict):
input_dict['obs'] = self._model.norm_obs(input_dict['obs'])
'''
just model export doesn't work. Looks like onnx issue with torch distributions
thats why we are exporting only neural network
'''
# print(input_dict)
# output_dict = self._model.a2c_network(input_dict)
# input_dict['is_train'] = False
# return output_dict['logits'], output_dict['values']
return self._model.a2c_network(input_dict)
# create runner and set the settings
runner = Runner(RLGPUAlgoObserver())
runner.load(self.rlg_config_dict)
runner.reset()
# dump config dict
experiment_dir = os.path.join('runs', self.cfg.train.params.config.name)
os.makedirs(experiment_dir, exist_ok=True)
with open(os.path.join(experiment_dir, 'config.yaml'), 'w') as f:
f.write(OmegaConf.to_yaml(self.cfg))
agent = runner.create_player()
agent.restore('/home/dylan/Desktop/repos/OmniIsaacGymEnvs-DofbotReacher/runs/DofbotReacher/nn/DofbotReacher.pth')
inputs = {
'obs': torch.zeros((1,) + agent.obs_shape).to(agent.device),
'rnn_states': agent.states,
}
with torch.no_grad():
adapter = flatten.TracingAdapter(ModelWrapper(agent.model), inputs, allow_non_tensor=True)
traced = torch.jit.trace(adapter, adapter.flattened_inputs, check_trace=False)
flattened_outputs = traced(*adapter.flattened_inputs)
print(flattened_outputs)
torch.onnx.export(traced, *adapter.flattened_inputs, "dofbotreacherDefault.onnx", verbose=True, input_names=['obs'],
output_names=['mu', 'log_std', 'value'])
runner.run({
'train': not self.cfg.test,
'play': self.cfg.test,
'checkpoint': self.cfg.checkpoint,
'sigma': None
})
But when I run this in a notebook to compare with the output of the model in Isaac Sim I am getting different results:
while True:
input_data = np.concatenate((jointPos, jointVel, goalPos, goalRot, goalRotRel, prevAct), dtype=np.float32)
input_dict = {input_name: input_data.reshape(1, -1)}
actions = []
output = sess.run(None, input_dict)
# Run the model on the input data
mus = output[0][0]
sigmas = output[1][0]
for mu, sigma in zip(mus, sigmas):
#print("mu is " + str(mu))
#print("sigma is " + str(sigma))
sigma = np.exp(sigma)
action = np.random.normal(mu, sigma)
action = clip_actions(action)
#print(str(action))
actions.append(action)
# Print the output
prevAct = np.array(actions, dtype=np.float32)
joinPos = prevAct
mus = clip_actions(mus)
print("From Mu: " + str(mus))
print("Sampled Actions: " + str(actions)+"\n")
My results from exported ONNX:
From Mu: [-1. -1. -1. -0.8046344 1. 0.03140484]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9712634764535495, 1.0, 0.13085964262265282]
From Mu: [-1. -1. -1. -0.7526153 0.3962637 -0.4832963]
Sampled Actions: [-1.0, -1.0, -1.0, -0.673332441238304, 0.4277837343190856, -0.8004600149667951]
From Mu: [-1. -1. -1. -0.7781227 1. 0.01266351]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9150055130283129, 1.0, 0.5220340378975656]
From Mu: [-1. -1. -1. -0.75321215 0.4043142 -0.48851803]
Sampled Actions: [-1.0, -1.0, -1.0, -0.740595988656948, 0.6001872415307878, -0.43890275166219206]
From Mu: [-1. -1. -1. -0.79603606 1. 0.02533225]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7589437855821842, 0.9001211409978054, 0.45835817870457995]
From Mu: [-1. -1. -1. -0.7440234 0.44310746 -0.50922453]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7435969780643082, 0.12811670977753048, -0.7244098554326204]
From Mu: [-1. -1. -1. -0.7519526 1. 0.01125173]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7532973499744635, 1.0, -0.40081315228268555]
From Mu: [-1. -1. -1. -0.83088285 0.99210644 0.0389543 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8324133591496548, 0.706666445481611, 0.19869922991846953]
From Mu: [-1. -1. -1. -0.7238199 0.46477643 -0.5139754 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.5591049802680348, 0.4525925046550397, -0.8477365401897607]
From Mu: [-1.0000000e+00 -1.0000000e+00 -1.0000000e+00 -7.7655083e-01
1.0000000e+00 8.5781515e-04]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9417623079946027, 1.0, 0.19555459127998726]
From Mu: [-1. -1. -1. -0.752935 0.40048864 -0.4860393 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7758784773062144, 0.2824237395466518, -0.6451741533484725]
From Mu: [-1. -1. -1. -0.7677086 1. 0.0195474]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7224911522708941, 1.0, 0.013253213240240352]
From Mu: [-1. -1. -1. -0.76368755 0.4935695 -0.4761499 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.711067045811111, 0.45817385742559213, -0.33301126978630297]
From Mu: [-1. -1. -1. -0.78214306 1. 0.01774313]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8200303164566594, 1.0, 0.06691762403021183]
From Mu: [-1. -1. -1. -0.7540979 0.41784108 -0.49724796]
Sampled Actions: [-1.0, -1.0, -1.0, -0.971417280472688, 0.24631265870297667, -0.420806805659078]
From Mu: [-1. -1. -1. -0.7697392 1. 0.04017575]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8351657458525144, 1.0, -0.3143587497067801]
From Mu: [-1. -1. -1. -0.83230096 0.9839373 0.04801101]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6531447447963699, 1.0, -0.5401700070822363]
From Mu: [-1. -1. -1. -0.82877606 1. 0.02789856]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7325868211515937, 1.0, -0.2056215451291925]
From Mu: [-1. -1. -1. -0.8304811 0.99420536 0.03666638]
Sampled Actions: [-0.9596524949814464, -1.0, -1.0, -0.727783274374398, 1.0, -0.022099674545825593]
From Mu: [-1. -1. -1. -0.83203524 0.98964196 0.02937362]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9240667476074332, 1.0, 0.14339611663589916]
From Mu: [-1. -1. -1. -0.75311977 0.4030195 -0.4876795 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6717779185202091, -0.18275692793347031, -0.36308104834416527]
From Mu: [-1. -1. -1. -0.7185393 1. -0.00630318]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6100275823929414, 1.0, 0.016816065477693667]
From Mu: [-1. -1. -1. -0.7555036 0.4474652 -0.51618 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7180995217046809, 1.0, -0.13469952553495773]
From Mu: [-1. -1. -1. -0.8301895 0.99568105 0.03506646]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7588848485509612, 0.9381901139756421, 0.31158853516230894]
From Mu: [-1. -1. -1. -0.74803036 0.4368003 -0.50681484]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7154093862788289, 0.8633711819668368, -1.0]
From Mu: [-1. -1. -1. -0.81857425 1. 0.03061933]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9605380108456176, 1.0, 0.224580063371297]
From Mu: [-1. -1. -1. -0.75273305 0.39780074 -0.48429456]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7599399310258989, 0.683415490179879, 0.10554475773474592]
From Mu: [-1. -1. -1. -0.7216615 0.47854942 -0.5221823 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8500937557664596, 0.44775010561508727, -0.062483218958119646]
From Mu: [-1. -1. -1. -0.78516716 1. 0.0327966 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6297888878813683, 0.8676291846536264, -0.044572628309073714]
From Mu: [-1. -1. -1. -0.8168054 1. 0.02131866]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9869841126204636, 0.749818234488756, -0.0865036930558632]
From Mu: [-1. -1. -1. -0.81412536 1. 0.0572635 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8774458476527681, 0.9262720416298681, -0.3105193372811678]
From Mu: [-1. -1. -1. -0.8269616 0.99148035 0.05044907]
Sampled Actions: [-1.0, -1.0, -1.0, -0.956779717342482, 1.0, -0.09833272962712514]
From Mu: [-1. -1. -1. -0.8339178 0.97218335 0.06150183]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8847926518222826, 0.7811633147768765, 0.6468734889113454]
From Mu: [-1. -1. -1. -0.7310789 0.4452337 -0.50467825]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7219534542825735, 0.6578086633571447, -0.5079010698438633]
From Mu: [-1. -1. -1. -0.80070335 1. 0.02505435]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7067711793896203, 1.0, 0.04010780364819768]
From Mu: [-1. -1. -1. -0.75495094 0.43386662 -0.50752133]
Sampled Actions: [-1.0, -1.0, -1.0, -0.734413969039702, 0.5941167050192434, -0.5086914582627033]
From Mu: [-1. -1. -1. -0.79531425 1. 0.02446295]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8421573316399269, 1.0, 0.30989272641437204]
From Mu: [-1. -1. -1. -0.75390536 0.4146967 -0.49522403]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9163837434115838, 0.31018013332085653, -0.6052221552721155]
From Mu: [-1. -1. -1. -0.77425617 1. 0.03597975]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6653456375410538, 1.0, -0.06353721821121802]
From Mu: [-1. -1. -1. -0.8290542 1. 0.02924436]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6330052804822425, 0.29690473715661647, -0.2526023832207899]
From Mu: [-1. -1. -1. -0.7643934 1. 0.00425477]
Sampled Actions: [-1.0, -1.0, -1.0, -0.644647362984077, 1.0, -0.039213324141717576]
From Mu: [-1. -1. -1. -0.82857746 1. 0.02696133]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6490890462102662, 0.9928362688072035, -0.020496762619332073]
From Mu: [-1. -1. -1. -0.8280749 1. 0.02723466]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7456473961946039, 1.0, -0.19916520520425451]
From Mu: [-1. -1. -1. -0.83073646 0.99288005 0.03810912]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6872249670162497, 1.0, -0.1842744928744335]
From Mu: [-1. -1. -1. -0.82953936 0.99884725 0.03165848]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7027429862791732, 1.0, 0.1565127741614349]
From Mu: [-1. -1. -1. -0.75497705 0.43443447 -0.5078842 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9300769908160136, 0.5547780652801778, -0.15824015832012317]
From Mu: [-1. -1. -1. -0.7966184 1. 0.04495192]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9351516547556644, 1.0, 0.41995618692489717]
From Mu: [-1. -1. -1. -0.75300497 0.4014346 -0.48665246]
Sampled Actions: [-1.0, -1.0, -1.0, -0.799439295000151, 0.33643433600371603, -0.1238350057988532]
From Mu: [-1. -1. -1. -0.7734776 1. 0.02380122]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7137572110777022, 1.0, 0.03234684937291753]
From Mu: [-1. -1. -1. -0.75490457 0.4328815 -0.50689197]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6739407059979791, 0.3401257993163045, -0.5932283956507928]
From Mu: [-1. -1. -1. -0.76990277 1. 0.01006894]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9218036317157808, 1.0, 0.19921441468520712]
From Mu: [-1. -1. -1. -0.753143 0.4033429 -0.48788902]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8677409427333201, 0.10603287720424603, -1.0]
From Mu: [-1. -1. -1. -0.7537337 1. 0.02431748]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7987103700020154, 0.9748768000782158, -0.19254955620029196]
From Mu: [-1. -1. -1. -0.82964367 0.99151677 0.04321043]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7772603301650468, 1.0, 0.08330701284505027]
From Mu: [-1. -1. -1. -0.7544461 0.42390615 -0.5011452 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7215235033994709, 0.7475780753105198, -0.4463962371186046]
From Mu: [-1. -1. -1. -0.8086596 1. 0.02776166]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7416580639653408, 0.7908667095687227, -0.7007411216283626]
From Mu: [-1. -1. -1. -0.8129317 1. 0.03130444]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9641518084761704, 0.7152171875335328, -0.1790646387606805]
From Mu: [-1. -1. -1. -0.8109068 1. 0.0536619]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9449071105724618, 0.9214226188393199, 0.3830525353828998]
From Mu: [-1. -1. -1. -0.7448434 0.41333145 -0.49061635]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6139587415753119, 0.27964950178425085, -0.805540133657932]
From Mu: [-1. -1. -1. -0.7620633 1. 0.00163948]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7513908686503362, 1.0, 0.23165483971241815]
From Mu: [-1. -1. -1. -0.754641 0.42756668 -0.5034921 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9274740125963947, 0.5669085915310174, -0.6694073326868109]
From Mu: [-1. -1. -1. -0.79761654 1. 0.04503421]
Sampled Actions: [-1.0, -1.0, -1.0, -0.829177380456725, 0.9792390638350275, 0.7460004057255771]
From Mu: [-1. -1. -1. -0.7518366 0.4200245 -0.49774817]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7531855640293652, 0.3724516385546356, -0.81092287920861]
From Mu: [-1. -1. -1. -0.77546066 1. 0.01978657]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8006624931542228, 1.0, -0.2035726014573656]
From Mu: [-1. -1. -1. -0.831736 0.9873554 0.04419161]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8211956396909531, 1.0, 0.3126758327660015]
From Mu: [-1. -1. -1. -0.75408775 0.41767544 -0.497142 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6773146200950056, 0.23898131031657155, -0.00888535581840777]
From Mu: [-1. -1. -1. -0.7500414 1. -0.12462715]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7044294522807475, 1.0, -0.37893207805010704]
From Mu: [-1. -1. -1. -0.8299064 0.9970793 0.03355742]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8042123596741024, 1.0, 0.02978162273492279]
From Mu: [-1. -1. -1. -0.7542301 0.42008585 -0.49869195]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7994773223681491, 0.35195154559923064, -0.6513128507643774]
From Mu: [-1. -1. -1. -0.77492505 1. 0.02427842]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8146382622800785, 1.0, 0.4632515711692034]
From Mu: [-1. -1. -1. -0.7541432 0.41860655 -0.49774054]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6399486189712743, 0.47991460152377413, -0.6106060048915335]
From Mu: [-1. -1. -1. -0.7818957 1. 0.0105686]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7521512450297217, 1.0, -0.101485989421196]
From Mu: [-1. -1. -1. -0.8308614 0.9922222 0.03882762]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8318709297241236, 0.7954753471454886, 0.18972583585456484]
From Mu: [-1. -1. -1. -0.7328147 0.45023966 -0.50883967]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7645353328437561, 0.8173021942662632, -0.4628886097273362]
From Mu: [-1. -1. -1. -0.8157343 1. 0.03463554]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8481815342708638, 1.0, 0.03233081407351638]
From Mu: [-1. -1. -1. -0.7538517 0.4138401 -0.4946716]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8667782367159919, 0.5177156208302721, -0.3209698993271739]
From Mu: [-1. -1. -1. -0.7919213 1. 0.03677167]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7166605067083957, 0.7848380977631081, -0.903113004857943]
From Mu: [-1. -1. -1. -0.8118021 1. 0.0283642]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7483624175285033, 0.9690469629087698, 0.3157288374427686]
From Mu: [-1. -1. -1. -0.75136656 0.43315125 -0.50578666]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7228498155618657, 0.8739042277649753, -0.8068623520948508]
From Mu: [-1. -1. -1. -0.8196482 1. 0.0317601]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7101558499317228, 1.0, 0.11094376540214405]
From Mu: [-1. -1. -1. -0.7549284 0.43338925 -0.5072165 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7380984420750469, 0.19013209276868123, -0.4474742833012317]
From Mu: [-1. -1. -1. -0.7577391 1. 0.01255114]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7153524683163135, 1.0, 0.42582526401823645]
From Mu: [-1. -1. -1. -0.7548941 0.43265638 -0.5067481 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8254276575165649, 0.7881192382061699, -0.5197676868750642]
From Mu: [-1. -1. -1. -0.8145303 1. 0.04047535]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8888925976178152, 0.5152770032077211, -1.0]
From Mu: [-1. -1. -1. -0.7922334 1. 0.03915513]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7928917736744503, 1.0, 0.7313546877440096]
From Mu: [-1. -1. -1. -0.75432247 0.4216915 -0.4997233 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7441526863909724, 0.38976482033239357, -0.06913269820327372]
From Mu: [-1. -1. -1. -0.77680117 1. 0.0193164 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7109446039021524, 1.0, 0.0463337171791479]
From Mu: [-1. -1. -1. -0.7549233 0.43327805 -0.50714546]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9411369457112222, 0.6229244546911966, -0.002945034717867112]
From Mu: [-1. -1. -1. -0.7737288 0.87156916 -0.20136413]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8333429132267878, 0.7866244967082299, -0.40523307862356206]
From Mu: [-1. -1. -1. -0.8145625 1. 0.04130588]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8137097281918882, 0.8703228699255615, -0.11254037521151103]
From Mu: [-1. -1. -1. -0.8212401 1. 0.04168546]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8345495408115013, 0.964417183788387, -0.08616398051944929]
From Mu: [-1. -1. -1. -0.8293955 0.989624 0.04685815]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9619933018895461, 0.9294147044207864, 0.26190701677129324]
From Mu: [-1. -1. -1. -0.7454954 0.40955535 -0.4885219 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7298318947599646, 0.7249129003673476, -0.44874197696222995]
From Mu: [-1. -1. -1. -0.80687493 1. 0.0279835 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9172371300540336, 1.0, -0.11950898063572937]
From Mu: [-1. -1. -1. -0.83345515 0.97595435 0.05710997]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7520426333517087, 0.7108703606577311, -0.2643116840263049]
From Mu: [-1. -1. -1. -0.80619746 1. 0.03000315]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8700702838087512, 1.0, -0.5868225448990657]
From Mu: [-1. -1. -1. -0.83282405 0.9805167 0.0518783 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8459504345277016, 0.852532244547307, 0.21485077773330233]
From Mu: [-1. -1. -1. -0.73856825 0.43881717 -0.5040919 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6042425399210104, 0.38998532721734425, -0.6512789363508023]
From Mu: [-1. -1. -1. -0.7722377 1. 0.00392138]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8335485796836798, 1.0, 0.6453908524881675]
From Mu: [-1. -1. -1. -0.7539808 0.41592062 -0.4960123 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6598709852418955, 0.3168695489524249, -0.836174569712293]
From Mu: [-1. -1. -1. -0.76722413 1. 0.00781376]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8826941159915369, 0.9815507997243897, 0.09223066650737678]
From Mu: [-1. -1. -1. -0.7516089 0.41203526 -0.49267414]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7262354023240403, 0.611571876077073, -0.3748896004425093]
From Mu: [-1. -1. -1. -0.7966631 1. 0.02409985]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8437298752685033, 1.0, -0.21390895491626874]
From Mu: [-1. -1. -1. -0.83243376 0.9830944 0.04895948]
Sampled Actions: [-1.0, -1.0, -1.0, -1.0, 0.5754243191292447, 0.23529930719799402]
From Mu: [-1. -1. -1. -0.71029884 0.463244 -0.50490886]
Sampled Actions: [-1.0, -1.0, -1.0, -0.73022477601822, 0.022821628964901475, -0.5316323107425646]
From Mu: [-1. -1. -1. -0.7412426 1. 0.00652762]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7626166359492124, 1.0, -0.06862099196679089]
From Mu: [-1. -1. -1. -0.83105797 0.99116623 0.03998423]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9733124724446618, 0.37299390582550784, 0.44632602453678194]
From Mu: [-1. -1. -1. -0.691082 0.49967584 -0.51800704]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6466158853593572, 0.10894997588414462, -1.0]
From Mu: [-1.0000000e+00 -1.0000000e+00 -1.0000000e+00 -7.4669188e-01
1.0000000e+00 -2.7045608e-06]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8063693254883623, 1.0, 0.5692572123634391]
From Mu: [-1. -1. -1. -0.7542121 0.41978022 -0.49849543]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7238280431509154, 0.38645082434177647, -0.9589964281916483]
From Mu: [-1. -1. -1. -0.77586526 1. 0.01697315]
Sampled Actions: [-1.0, -1.0, -1.0, -0.831368703825368, 1.0, -0.5139026712857898]
From Mu: [-1. -1. -1. -0.8322415 0.9843118 0.04759049]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7360280364090481, 0.9232017585720846, 0.5881593626744092]
From Mu: [-1. -1. -1. -0.746584 0.4424964 -0.5098611]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6488643998651968, 0.2686077691383384, -0.3111219097689356]
From Mu: [-1. -1. -1. -0.76224035 1. 0.00513411]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8595738078202146, 1.0, -0.512847792326814]
From Mu: [-1. -1. -1. -0.8326718 0.98154134 0.05071499]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8643575460178847, 0.862350834502499, -0.38874976606175027]
From Mu: [-1. -1. -1. -0.8214912 1. 0.04705023]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6588649259331928, 1.0, -0.5046605473797209]
From Mu: [-1. -1. -1. -0.8289071 1. 0.02852955]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8517433537320316, 1.0, 0.05951976909559868]
From Mu: [-1. -1. -1. -0.75381917 0.41333315 -0.4943451 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6605816309924912, 0.5240264577972836, -0.36094379048087666]
From Mu: [-1. -1. -1. -0.7866599 1. 0.01418263]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8125747178432652, 1.0, -0.6301311988732121]
From Mu: [-1. -1. -1. -0.8319364 0.98617125 0.04550968]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9180389179816161, 1.0, 0.28216419973977785]
From Mu: [-1. -1. -1. -0.7531814 0.4038807 -0.4882373]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6380547114189727, 0.03953814606399447, -0.2504484085064785]
From Mu: [-1. -1. -1. -0.7395368 1. -0.0030805]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6973475279789967, 0.8115279369858396, 0.07558269950511493]
From Mu: [-1. -1. -1. -0.735009 0.46627623 -0.520403 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.622644426546135, 0.4373438596924615, -0.08400922156111618]
From Mu: [-1. -1. -1. -0.77733594 1. 0.00737545]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7586348374902103, 0.7959879168119375, 0.17666705980918532]
From Mu: [-1. -1. -1. -0.7331888 0.46033216 -0.51566833]
Sampled Actions: [-1.0, -1.0, -1.0, -0.5334251034131978, 0.3052061611582506, -0.5437402490291854]
From Mu: [-1. -1. -1. -0.7615332 1. -0.00640586]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7560654529868349, 1.0, 0.539021922370829]
From Mu: [-1. -1. -1. -0.7546068 0.4269057 -0.5030684]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8423875899476196, 0.4653336632067847, -1.0]
From Mu: [-1. -1. -1. -0.7865687 1. 0.03247565]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7663988395721106, 1.0, -0.17325005915778452]
From Mu: [-1. -1. -1. -0.83112794 0.99078536 0.04040235]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9676105318614661, 0.8365514947440982, 0.5571944694536217]
From Mu: [-1. -1. -1. -0.7360707 0.4243949 -0.49354514]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7366622475797552, 0.390995301230382, -0.03810309075937229]
From Mu: [-1. -1. -1. -0.77668625 1. 0.01852713]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8130309624714379, 1.0, 0.44082986876843167]
From Mu: [-1. -1. -1. -0.75415725 0.41883454 -0.49788752]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6394138721748022, 0.5443130386856704, -0.4965374936603323]
From Mu: [-1. -1. -1. -0.78786045 1. 0.01247414]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8427138518191807, 1.0, 0.16638944786908302]
From Mu: [-1. -1. -1. -0.75390023 0.41461757 -0.49517307]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6390485261141253, 0.5640379344859947, -0.5100361164760574]
From Mu: [-1. -1. -1. -0.78967327 1. 0.01303856]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7034092600547694, 0.7546028299960432, 0.05844635706625497]
From Mu: [-1. -1. -1. -0.72906953 0.47473878 -0.5232934 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.5826544421244291, 0.5793368480820541, -0.4019668633045341]
From Mu: [-1. -1. -1. -0.78923744 1. 0.00731073]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6381532763226888, 1.0, 0.3110264941862362]
From Mu: [-1. -1. -1. -0.7553595 0.4435204 -0.51367366]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7857085545448248, 0.494469254950458, -0.6016415142932836]
From Mu: [-1. -1. -1. -0.78768235 1. 0.02708494]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8083727740193192, 1.0, -0.24852696109124245]
From Mu: [-1. -1. -1. -0.83186644 0.98658824 0.04504448]
Sampled Actions: [-1.0, -1.0, -1.0, -0.776346378269931, 1.0, -0.28950093764337215]
From Mu: [-1. -1. -1. -0.8313095 0.98978597 0.04150212]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6960893478644005, 0.9014041015396248, -0.22198072487502907]
From Mu: [-1. -1. -1. -0.82138604 1. 0.02964587]
Sampled Actions: [-0.9768299492942232, -1.0, -1.0, -0.8759363600846799, 1.0, 0.12790216985630348]
From Mu: [-1. -1. -1. -0.75762206 0.40363485 -0.49327156]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7063905887776372, 0.20395591216468664, -0.486436042214649]
From Mu: [-1. -1. -1. -0.75801116 1. 0.00948197]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9542299599565807, 1.0, 0.11332216743685222]
From Mu: [-1. -1. -1. -0.75280184 0.39870414 -0.4848815 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6944751518937373, 0.20959305591299868, 0.04993119163857018]
From Mu: [-1. -1. -1. -0.67443234 0.5622158 -0.55371433]
Sampled Actions: [-1.0, -1.0, -1.0, -0.665550245298805, 0.38887023213162863, -0.1318197420317252]
From Mu: [-1. -1. -1. -0.7742209 1. 0.01062669]
Sampled Actions: [-1.0, -1.0, -1.0, -0.745085411438627, 0.9742014617465792, -0.4184609329492054]
From Mu: [-1. -1. -1. -0.8285844 0.9970036 0.03726296]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7205167559993333, 0.8334237806172715, -0.26492267600600583]
From Mu: [-1. -1. -1. -0.81611484 1. 0.03027082]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8613384701109654, 0.9797665377255256, 0.24349944851927657]
From Mu: [-1. -1. -1. -0.7516162 0.41537166 -0.49475768]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8702310431423083, 0.22695640724913071, -0.7334373026744053]
From Mu: [-1. -1. -1. -0.765258 1. 0.02830486]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7504270744637468, 1.0, -0.3195996561245299]
From Mu: [-1. -1. -1. -0.83082813 0.9923965 0.03863757]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8663857049559778, 0.5733008315297083, 0.7173743299851452]
From Mu: [-1. -1. -1. -0.71049124 0.48186427 -0.51813126]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8041422951433205, 0.5554909080842381, 0.20613152230278142]
From Mu: [-1. -1. -1. -0.70880026 0.49321756 -0.5251183 ]
Sampled Actions: [-1.0, -1.0, -1.0, -0.614003760194867, 0.7653976137533172, -0.9266509000795795]
From Mu: [-1. -1. -1. -0.80729204 1. 0.01647381]
Sampled Actions: [-1.0, -1.0, -1.0, -0.6881985439225536, 0.9390496989627011, -0.2663311783688894]
From Mu: [-1. -1. -1. -0.8244148 1. 0.02991825]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8310000061950585, 1.0, -0.31772355246662787]
From Mu: [-1. -1. -1. -0.8322353 0.98434806 0.04754952]
Sampled Actions: [-1.0, -1.0, -1.0, -0.9037621099534194, 0.8333114817000511, -0.6050761211733053]
From Mu: [-1. -1. -1. -0.81974214 1. 0.05053739]
Sampled Actions: [-1.0, -1.0, -1.0, -0.8060724344365541, 1.0, -0.17339006375675628]
From Mu: [-1. -1. -1. -0.8318277 0.98681706 0.04479015]
Sampled Actions: [-1.0, -1.0, -1.0, -1.0, 0.8159709382943136, 0.024050554393333336]
From Mu: [-1. -1. -1. -0.7337516 0.4232876 -0.4916418]
Sampled Actions: [-1.0, -1.0, -1.0, -0.7702245885232373, 0.5973745937048496, -0.26677264893490593]
My results from the IsaacSim:
new position tensor([[0.1949, 0.1820, 0.2187]], device='cuda:0')
tensor([[-0.7803, -1.0000, -1.0000, -1.0000, 1.0000, -0.5157]],
device='cuda:0')
tensor([[-0.5791, -1.0000, -1.0000, -1.0000, 0.3410, -0.5124]],
device='cuda:0')
tensor([[-0.4997, -1.0000, -1.0000, -1.0000, 0.2800, -0.5737]],
device='cuda:0')
tensor([[-0.4843, -1.0000, -1.0000, -1.0000, 0.2260, -0.5793]],
device='cuda:0')
tensor([[-0.5000, -1.0000, -1.0000, -1.0000, 0.1828, -0.5856]],
device='cuda:0')
tensor([[-0.5323, -1.0000, -1.0000, -1.0000, 0.1400, -0.5825]],
device='cuda:0')
tensor([[-0.5508, -1.0000, -1.0000, -1.0000, 0.1113, -0.6239]],
device='cuda:0')
tensor([[-0.6025, -1.0000, -1.0000, -1.0000, 0.0443, -0.5531]],
device='cuda:0')
tensor([[-0.5848, -1.0000, -1.0000, -1.0000, 0.0372, -0.6465]],
device='cuda:0')
tensor([[-0.5442, -1.0000, -1.0000, -1.0000, 0.0082, -0.6643]],
device='cuda:0')
tensor([[-0.4736, -1.0000, -1.0000, -1.0000, -0.0145, -0.7116]],
device='cuda:0')
tensor([[-0.5291, -1.0000, -1.0000, -1.0000, -0.0703, -0.7217]],
device='cuda:0')
tensor([[-0.4950, -1.0000, -1.0000, -1.0000, -0.0821, -0.7635]],
device='cuda:0')
tensor([[-0.5306, -1.0000, -1.0000, -1.0000, -0.1220, -0.8100]],
device='cuda:0')
tensor([[-0.5168, -1.0000, -1.0000, -1.0000, -0.1207, -0.8285]],
device='cuda:0')
tensor([[-0.5442, -1.0000, -1.0000, -1.0000, -0.0972, -0.8447]],
device='cuda:0')
tensor([[-0.5368, -1.0000, -1.0000, -1.0000, -0.0495, -0.7873]],
device='cuda:0')
tensor([[-0.5217, -1.0000, -1.0000, -1.0000, 0.0289, -0.7510]],
device='cuda:0')
tensor([[-0.4257, -0.7880, -1.0000, -1.0000, 0.1470, -0.6592]],
device='cuda:0')
tensor([[-0.2987, -0.0884, -1.0000, -1.0000, 0.2608, -0.5015]],
device='cuda:0')
tensor([[-0.4287, 0.1850, -1.0000, -1.0000, -0.2809, -0.4096]],
device='cuda:0')
tensor([[-0.5971, 0.0153, -1.0000, -1.0000, -0.1758, -0.5567]],
device='cuda:0')
tensor([[-0.6339, -0.6090, -1.0000, -1.0000, -0.1512, -0.5023]],
device='cuda:0')
tensor([[-0.3895, -0.3582, -1.0000, -1.0000, 0.3381, -0.3637]],
device='cuda:0')
tensor([[-0.2472, 0.5666, -1.0000, -0.9056, 0.4192, -0.1518]],
device='cuda:0')
tensor([[-0.5179, 0.4278, -0.8854, -0.8633, -0.1279, -0.2017]],
device='cuda:0')
tensor([[-0.7538, -0.2676, -1.0000, -1.0000, -0.1480, -0.3828]],
device='cuda:0')
tensor([[-0.5746, -0.3179, -1.0000, -1.0000, 0.1659, -0.4121]],
device='cuda:0')
tensor([[-0.3536, 0.1061, -1.0000, -1.0000, 0.2730, -0.0825]],
device='cuda:0')
tensor([[-0.4643, 0.4024, -1.0000, -0.9495, -0.2056, -0.2248]],
device='cuda:0')
tensor([[-0.6804, -0.0439, -1.0000, -1.0000, -0.2589, -0.5425]],
device='cuda:0')
tensor([[-0.6613, -0.5530, -1.0000, -1.0000, -0.1297, -0.4616]],
device='cuda:0')
tensor([[-0.4080, -0.3370, -1.0000, -1.0000, 0.2396, -0.3226]],
device='cuda:0')
tensor([[-0.3005, 0.5082, -1.0000, -0.9445, 0.3195, -0.1575]],
device='cuda:0')
tensor([[-0.5592, 0.3544, -0.8949, -0.9353, -0.2282, -0.2062]],
device='cuda:0')
tensor([[-0.7875, -0.3733, -1.0000, -1.0000, -0.2678, -0.4217]],
device='cuda:0')
tensor([[-0.5984, -0.5766, -1.0000, -1.0000, 0.0464, -0.5609]],
device='cuda:0')
tensor([[-0.2753, -0.0271, -1.0000, -1.0000, 0.3200, -0.1748]],
device='cuda:0')
tensor([[-0.3842, 0.4044, -1.0000, -0.9492, -0.2539, -0.2206]],
device='cuda:0')
tensor([[-6.4951e-01, -4.9989e-04, -1.0000e+00, -1.0000e+00, -2.5767e-01,
-5.5067e-01]], device='cuda:0')
tensor([[-0.6404, -0.5183, -1.0000, -1.0000, -0.1491, -0.4507]],
device='cuda:0')
tensor([[-0.3876, -0.3133, -1.0000, -1.0000, 0.2379, -0.3083]],
device='cuda:0')
tensor([[-0.2889, 0.4871, -1.0000, -0.9488, 0.2882, -0.1684]],
device='cuda:0')
tensor([[-0.5556, 0.3315, -0.8842, -0.9432, -0.2478, -0.2145]],
device='cuda:0')
tensor([[-0.7738, -0.3490, -1.0000, -1.0000, -0.2870, -0.4362]],
device='cuda:0')
tensor([[-0.5808, -0.5886, -1.0000, -1.0000, 0.0482, -0.5692]],
device='cuda:0')
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Can anyone help me figure out how to export a policy and get it running externally from Isaaac Sim? I think this would be a great problem to solve and would help a lot of people.
I’ve tried contacting the repo owner here:
opened 06:57PM - 11 Feb 23 UTC
Hi,
I am working through the training example for Dofbot reacher, and wanting… to deploy it to a real arm.
I am wondering how I will be able to export the saved .pth to an actual dofbot and run it. What kinds of inputs to the loaded model from the .pth will I need to give it, and what will the output be?
I’m aware that I may be able to use PyTorch to load the model, but I don’t know what kinds of inputs I will need to give it.
In the [Config](https://github.com/j3soon/OmniIsaacGymEnvs-DofbotReacher/blob/0f5fdb7a5687e8f78392f599989a46372b534c23/omniisaacgymenvs/cfg/task/DofbotReacher.yaml) for this task the observation type is set to full. What exactly does the “full” observation look like?
I'm assuming that the full state is like this based on the dofbot_reacher.py code
self.num_obs_dict = {
"full": 29,
# 6: dofbot joints position (action space)
# 6: dofbot joints velocity
# 3: goal position
# 4: goal rotation
# 4: goal relative rotation
# 6: previous action
}
Can you explain to me what each of these values in the state dictionary are?