def get_obs(self):
xs=[]
sim = arcsim.get_sim()
for node in sim.cloths[0].mesh.nodes:
xs.append(torch.stack([torch.tensor(arcsim.tovec(node.x),dtype=torch.float64),torch.tensor(arcsim.tovec(node.v),dtype=torch.float64)]))
ans = torch.stack(xs).flatten().squeeze()
return ans.detach().numpy()
def reset(self):
with torch.no_grad():
arcsim.init_physics('../200120_manipulation/conf.json',
'../200120_manipulation/out_ddpg', False)
self.step_ = 0
self.sim = arcsim.get_sim()
sigma = 0.4
x = np.random.random() * sigma - 0.5 * sigma + np.random.randint(
2) * 2 - 1
self.goal = torch.tensor([
0.0000, 0.0000, 0.0000, x, 0,
2 + np.random.random() * sigma - 0.5 * sigma
],
dtype=torch.float64)
observation = []
remain_time = torch.tensor([(self.steps) / 50],
dtype=torch.float64)
observation = torch.cat([
self.sim.obstacles[0].curr_state_mesh.dummy_node.x - self.goal,
self.sim.obstacles[0].curr_state_mesh.dummy_node.v, remain_time
])
return observation.numpy()
def get_loss(self):
diffs = []
sim = arcsim.get_sim()
for node0 in sim.cloths[0].mesh.nodes:
diffs.append(torch.tensor(arcsim.tovec(node0.x),dtype=torch.float64))
dis = torch.stack(diffs).mean(dim=0)
dis[2] = dis[2] + 0.7
return dis.norm().detach().item()
def __init__(self, config):
sim = arcsim.get_sim()
self.action_space = Box(low=-30.,
high=30.,
shape=(4 * 3, ),
dtype=np.float64)
self.observation_space = Box(low=-100.0,
high=100.0,
shape=(81 * 2 * 3, ),
dtype=np.float64)
def reset(self):
with torch.no_grad():
self.step_ = 0
sigma = 0.1
z = np.random.random() * sigma + 0.4
y = np.random.random() * sigma - sigma / 2
x = np.random.random() * sigma - sigma / 2
ini_co = torch.tensor(
[0.0000, 0.0000, 0.0000, 0.4744, 0.4751, 0.0064],
dtype=torch.float64)
goal = torch.tensor([0.0000, 0.0000, 0.0000, 0, 0, z],
dtype=torch.float64)
goal = goal + ini_co
self.goal = goal
if self.epoch % 4 == 0 and self.epoch <= 60:
arcsim.init_physics('../200121_drag/conf.json',
'../200121_drag/out_ddpg%d' % self.epoch,
False)
text_name = '../200121_drag/out_ddpg%d' % self.epoch + "/goal.txt"
np.savetxt(text_name, goal[3:6], delimiter=',')
else:
arcsim.init_physics('../200121_drag/conf.json',
'../200121_drag/out_ddpg', False)
self.sim = arcsim.get_sim()
handles = [0, 1, 2, 3]
observation = []
remain_time = torch.tensor([(self.steps) / 50],
dtype=torch.float64)
for i in range(len(handles)):
observation.append(self.sim.cloths[0].mesh.nodes[handles[i]].x)
observation.append(self.sim.cloths[0].mesh.nodes[handles[i]].v)
dis = self.sim.obstacles[0].curr_state_mesh.dummy_node.x - self.goal
observation.append(dis.narrow(0, 3, 3))
observation.append(remain_time)
observation = torch.cat(observation)
# observation = []
# remain_time = torch.tensor([(self.steps)/50],dtype=torch.float64)
# observation = torch.cat([self.sim.obstacles[0].curr_state_mesh.dummy_node.x - self.goal,
# self.sim.obstacles[0].curr_state_mesh.dummy_node.v,
# remain_time])
return observation.detach().numpy()
def step(self, action):
sim = arcsim.get_sim()
print("step!",sim.step)
print(action.reshape([4,3]))
for _ in range(spf*25):
sec = int(sim.frame/25)
if sec < 3:
for i in range(4):
for k in range(3):
sim.cloths[0].mesh.nodes[i].v[k] = sim.cloths[0].mesh.nodes[i].v[k] + action[i*3+k]*scalev/spf
arcsim.sim_step()
sec = int(sim.frame/25)
if sec == 3:
for _ in range(spf*25):
arcsim.sim_step()
done = sim.step >= 4*25*spf
return self.get_obs(), -self.get_loss() if done else 0, done, {}
def reset(self):
with torch.no_grad():
if self.epoch <= 20:
arcsim.init_physics(
'../200216_bounce/conf.json',
'../200216_bounce/out_cmaes%d' % self.epoch, False)
text_name = '../200216_bounce/out_cmaes%d' % self.epoch + "/goal.txt"
goal = torch.tensor([0.0000, 0.0000, 0.0000, 0, 0, 0],
dtype=torch.float64)
np.savetxt(text_name, goal[3:6], delimiter=',')
else:
arcsim.init_physics('../200216_bounce/conf.json',
'../200216_bounce/out_cmaes', False)
self.step_ = 0
self.sim = arcsim.get_sim()
def reset(self):
with torch.no_grad():
self.step_ = 0
sigma = 0.4
x = np.random.random()*sigma - 0.5*sigma + np.random.randint(2)*2-1
ini_co = torch.tensor([0, 0, 0, 4.5549e-04, -2.6878e-01, 0.23], dtype=torch.float64)
goal = ini_co + torch.tensor([0.0000, 0.0000, 0.0000,
x,
0,
2+np.random.random()*sigma - 0.5*sigma],dtype=torch.float64)
self.goal = goal
# torch.tensor([0.0000, 0.0000, 0.0000,
# x,
# 0,
# 2+np.random.random()*sigma - 0.5*sigma],dtype=torch.float64)
if self.epoch % 4==0 and self.epoch <= 100:
arcsim.init_physics('../200216_man_vid/conf.json','../200216_man_vid/out_ddpg%d'%self.epoch,False)
text_name = '../200216_man_vid/out_ddpg%d'%self.epoch+ "/goal.txt"
np.savetxt(text_name, self.goal[3:6], delimiter=',')
else:
arcsim.init_physics('../200216_man_vid/conf.json', '../200216_man_vid/out_ddpg',False)
self.sim = arcsim.get_sim()
observation = []
remain_time = torch.tensor([(self.steps)/50],dtype=torch.float64)
observation = torch.cat([self.sim.obstacles[0].curr_state_mesh.dummy_node.x - self.goal,
self.sim.obstacles[0].curr_state_mesh.dummy_node.v,
remain_time])
return observation.numpy()
# dgrad, stgrad, begrad = torch.autograd.grad(loss, [density, stretch, bend])
optimizer.step()
epoch = epoch + 1
if epoch>=200:
quit()
# break
with open(out_path+('/log%s.txt'%timestamp),'w',buffering=1) as f:
tot_step = 1
sim=arcsim.get_sim()
reset_sim(sim)
#param_g = torch.tensor([0,0,0,0,0,1],dtype=torch.float64, requires_grad=True)
net = Net(13, 3)
if os.path.exists(torch_model_path):
net.load_state_dict(torch.load(torch_model_path))
print("load: %s\n success" % torch_model_path)
lr = 0.001
momentum = 0.6
f.write('lr={} momentum={}\n'.format(lr,momentum))
#optimizer = torch.optim.SGD([{'params':net.parameters(),'lr':lr}],momentum=momentum)
optimizer = torch.optim.Adam(net.parameters(),lr=lr)
# optimizer = torch.optim.Adadelta([density, stretch, bend])
for cur_step in range(tot_step):
def reset(self):
with torch.no_grad():
arcsim.init_physics('../200121_bounce/conf.json', '../200121_bounce/out_cmaes',False)
self.step_ = 0
self.sim = arcsim.get_sim()