add kukaCamGymEnv.py with camera observations (preliminary)

show camera position in example browser
disable per-vertex and per-fragment profile timings

examples/ExampleBrowser/OpenGLExampleBrowser.cpp

@@ -1296,8 +1296,10 @@ void OpenGLExampleBrowser::update(float deltaTime)
 				float pitch = s_guiHelper->getRenderInterface()->getActiveCamera()->getCameraPitch();
 				float yaw = s_guiHelper->getRenderInterface()->getActiveCamera()->getCameraYaw();
 				float camTarget[3];
+				float camPos[3];
+				s_guiHelper->getRenderInterface()->getActiveCamera()->getCameraPosition(camPos);
 				s_guiHelper->getRenderInterface()->getActiveCamera()->getCameraTargetPosition(camTarget);
-				sprintf(msg,"dist=%f, pitch=%f, yaw=%f,target=%f,%f,%f", camDist,pitch,yaw,camTarget[0],camTarget[1],camTarget[2]);
+				sprintf(msg,"camPos=%f,%f,%f, dist=%f, pitch=%f, yaw=%f,target=%f,%f,%f", camPos[0],camPos[1],camPos[2],camDist,pitch,yaw,camTarget[0],camTarget[1],camTarget[2]);
 				gui2->setStatusBarMessage(msg, true);	
 			}
 			

examples/TinyRenderer/TinyRenderer.cpp

@@ -124,7 +124,7 @@ struct Shader : public IShader {
 		m_projectionLightViewMat = m_projectionMat*m_lightModelView;
     }
     virtual Vec4f vertex(int iface, int nthvert) {
-		B3_PROFILE("vertex");
+		//B3_PROFILE("vertex");
         Vec2f uv = m_model->uv(iface, nthvert);
         varying_uv.set_col(nthvert, uv);
         varying_nrm.set_col(nthvert, proj<3>(m_invModelMat*embed<4>(m_model->normal(iface, nthvert), 0.f)));
@@ -142,7 +142,7 @@ struct Shader : public IShader {
     }
     
     virtual bool fragment(Vec3f bar, TGAColor &color) {
-		B3_PROFILE("fragment");
+		//B3_PROFILE("fragment");
         Vec4f p = m_viewportMat*(varying_tri_light_view*bar);
         float depth = p[2];
         p = p/p[3];
@@ -485,6 +485,7 @@ static bool clipTriangleAgainstNearplane(const mat<4,3,float>& triangleIn, b3Ali
 
 void TinyRenderer::renderObject(TinyRenderObjectData& renderData)
 {
+	B3_PROFILE("renderObject");
     int width = renderData.m_rgbColorBuffer.get_width();
     int height = renderData.m_rgbColorBuffer.get_height();
     
@@ -517,10 +518,11 @@ void TinyRenderer::renderObject(TinyRenderObjectData& renderData)
         
         Shader shader(model, light_dir_local, light_color, modelViewMatrix, lightModelViewMatrix, renderData.m_projectionMatrix,renderData.m_modelMatrix, renderData.m_viewportMatrix, localScaling, model->getColorRGBA(), width, height, shadowBufferPtr, renderData.m_lightAmbientCoeff, renderData.m_lightDiffuseCoeff, renderData.m_lightSpecularCoeff);
        
-		
+		{
+		B3_PROFILE("face");
+
         for (int i=0; i<model->nfaces(); i++)
         {
-			B3_PROFILE("face");
             for (int j=0; j<3; j++) {
                 shader.vertex(i, j);
             }
@@ -552,6 +554,7 @@ void TinyRenderer::renderObject(TinyRenderObjectData& renderData)
 				triangle(shader.varying_tri, shader, frame, &zbuffer[0], segmentationMaskBufferPtr, renderData.m_viewportMatrix, renderData.m_objectIndex);
 			}
         }
+		}
     }
     
 }

examples/pybullet/gym/envs/__init__.py

@@ -42,4 +42,11 @@ register(
     entry_point='envs.bullet:KukaGymEnv',
     timestep_limit=1000,
     reward_threshold=5.0,
+)
+
+register(
+    id='KukaCamBulletEnv-v0',
+    entry_point='envs.bullet:KukaCamGymEnv',
+    timestep_limit=1000,
+    reward_threshold=5.0,
 )

examples/pybullet/gym/envs/bullet/__init__.py

@@ -4,3 +4,4 @@ from envs.bullet.racecarGymEnv import RacecarGymEnv
 from envs.bullet.racecarZEDGymEnv import RacecarZEDGymEnv
 from envs.bullet.humanoidGymEnv import HumanoidGymEnv
 from envs.bullet.kukaGymEnv import KukaGymEnv
+from envs.bullet.kukaCamGymEnv import KukaCamGymEnv

examples/pybullet/gym/envs/bullet/kukaCamGymEnv.py

@@ -0,0 +1,191 @@
+import math
+import gym
+from gym import spaces
+from gym.utils import seeding
+import numpy as np
+import time
+import pybullet as p
+from . import kuka
+import random
+
+class KukaCamGymEnv(gym.Env):
+  metadata = {
+      'render.modes': ['human', 'rgb_array'],
+      'video.frames_per_second' : 50
+  }
+
+  def __init__(self,
+               urdfRoot="",
+               actionRepeat=1,
+               isEnableSelfCollision=True,
+               renders=True):
+    print("init")
+    self._timeStep = 1./240.
+    self._urdfRoot = urdfRoot
+    self._actionRepeat = actionRepeat
+    self._isEnableSelfCollision = isEnableSelfCollision
+    self._observation = []
+    self._envStepCounter = 0
+    self._renders = renders
+    self._width = 341
+    self._height = 256
+    self.terminated = 0
+    self._p = p
+    if self._renders:
+      p.connect(p.GUI)
+      p.resetDebugVisualizerCamera(1.3,180,-41,[0.52,-0.2,-0.33])
+    else:
+      p.connect(p.DIRECT)
+    #timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "kukaTimings.json")
+    self._seed()
+    self.reset()
+    observationDim = len(self.getExtendedObservation())
+    #print("observationDim")
+    #print(observationDim)
+    
+    observation_high = np.array([np.finfo(np.float32).max] * observationDim)    
+    self.action_space = spaces.Discrete(7)
+    self.observation_space = spaces.Box(low=0, high=255, shape=(self._height, self._width, 4))
+    self.viewer = None
+
+  def _reset(self):
+    print("reset")
+    self.terminated = 0
+    p.resetSimulation()
+    p.setPhysicsEngineParameter(numSolverIterations=150)
+    p.setTimeStep(self._timeStep)
+    p.loadURDF("%splane.urdf" % self._urdfRoot,[0,0,-1])
+    
+    p.loadURDF("table/table.urdf", 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0)
+    
+    xpos = 0.5 +0.05*random.random()
+    ypos = 0 +0.05*random.random()
+    ang = 3.1415925438*random.random()
+    orn = p.getQuaternionFromEuler([0,0,ang])
+    self.blockUid =p.loadURDF("block.urdf", xpos,ypos,-0.1,orn[0],orn[1],orn[2],orn[3])
+            
+    p.setGravity(0,0,-10)
+    self._kuka = kuka.Kuka(urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
+    self._envStepCounter = 0
+    p.stepSimulation()
+    self._observation = self.getExtendedObservation()
+    return np.array(self._observation)
+
+  def __del__(self):
+    p.disconnect()
+
+  def _seed(self, seed=None):
+    self.np_random, seed = seeding.np_random(seed)
+    return [seed]
+
+  def getExtendedObservation(self):
+     
+     #camEyePos = [0.03,0.236,0.54]
+     #distance = 1.06
+     #pitch=-56
+     #yaw = 258
+     #roll=0
+     #upAxisIndex = 2
+     #camInfo = p.getDebugVisualizerCamera()
+     #print("width,height")
+     #print(camInfo[0])
+     #print(camInfo[1])
+     #print("viewMatrix")
+     #print(camInfo[2])
+     #print("projectionMatrix")
+     #print(camInfo[3])
+     #viewMat = camInfo[2]
+     #viewMat = p.computeViewMatrixFromYawPitchRoll(camEyePos,distance,yaw, pitch,roll,upAxisIndex)
+     viewMat = [-0.5120397806167603, 0.7171027660369873, -0.47284144163131714, 0.0, -0.8589617609977722, -0.42747554183006287, 0.28186774253845215, 0.0, 0.0, 0.5504802465438843, 0.8348482847213745, 0.0, 0.1925382763147354, -0.24935829639434814, -0.4401884973049164, 1.0]
+     #projMatrix = camInfo[3]#[0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
+     projMatrix = [0.75, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
+     
+     img_arr = p.getCameraImage(width=self._width,height=self._height,viewMatrix=viewMat,projectionMatrix=projMatrix)
+     rgb=img_arr[2]
+     np_img_arr = np.reshape(rgb, (self._height, self._width, 4))
+     self._observation = np_img_arr
+     return self._observation
+  
+  def _step(self, action):
+    dv = 0.01
+    dx = [0,-dv,dv,0,0,0,0][action]
+    dy = [0,0,0,-dv,dv,0,0][action]
+    da = [0,0,0,0,0,-0.1,0.1][action]
+    f = 0.3
+    realAction = [dx,dy,-0.002,da,f]
+    return self.step2( realAction)
+     
+  def step2(self, action):
+    self._kuka.applyAction(action)
+    for i in range(self._actionRepeat):
+      p.stepSimulation()
+      if self._renders:
+        time.sleep(self._timeStep)
+      self._observation = self.getExtendedObservation()
+      if self._termination():
+        break
+      self._envStepCounter += 1
+    #print("self._envStepCounter")
+    #print(self._envStepCounter)
+    
+    done = self._termination()
+    reward = self._reward()
+    #print("len=%r" % len(self._observation))
+    
+    return np.array(self._observation), reward, done, {}
+
+  def _render(self, mode='human', close=False):
+      return
+
+  def _termination(self):
+    #print (self._kuka.endEffectorPos[2])
+    state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex)
+    actualEndEffectorPos = state[0]
+      
+    #print("self._envStepCounter")
+    #print(self._envStepCounter)
+    if (self.terminated or self._envStepCounter>1000):
+      self._observation = self.getExtendedObservation()
+      return True
+    
+    if (actualEndEffectorPos[2] <= 0.10):
+      self.terminated = 1
+      
+      #print("closing gripper, attempting grasp")
+      #start grasp and terminate
+      fingerAngle = 0.3
+      
+      for i in range (1000):
+        graspAction = [0,0,0.001,0,fingerAngle]
+        self._kuka.applyAction(graspAction)
+        p.stepSimulation()
+        fingerAngle = fingerAngle-(0.3/100.)
+        if (fingerAngle<0):
+          fingerAngle=0
+        
+      self._observation = self.getExtendedObservation()
+      return True
+    return False
+    
+  def _reward(self):
+    
+    #rewards is height of target object
+    blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid)
+    closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000) 
+
+    reward = -1000    
+    numPt = len(closestPoints)
+    #print(numPt)
+    if (numPt>0):
+      #print("reward:")
+      reward = -closestPoints[0][8]*10
+
+    if (blockPos[2] >0.2):
+      print("grasped a block!!!")
+      print("self._envStepCounter")
+      print(self._envStepCounter)
+      reward = reward+1000
+
+    #print("reward")
+    #print(reward)
+    return reward

examples/pybullet/gym/train_kuka_cam_grasping.py

@@ -0,0 +1,44 @@
+import gym
+from envs.bullet.kukaCamGymEnv import KukaCamGymEnv
+
+from baselines import deepq
+
+import datetime
+
+
+
+def callback(lcl, glb):
+    # stop training if reward exceeds 199
+    total = sum(lcl['episode_rewards'][-101:-1]) / 100
+    totalt = lcl['t']
+    #print("totalt")
+    #print(totalt)
+    is_solved = totalt > 2000 and total >= 10
+    return is_solved
+
+
+def main():
+  	
+    env = KukaCamGymEnv(renders=True)
+    model = deepq.models.cnn_to_mlp(
+        convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
+        hiddens=[256],
+        dueling=False
+    )
+    act = deepq.learn(
+        env,
+        q_func=model,
+        lr=1e-3,
+        max_timesteps=10000000,
+        buffer_size=50000,
+        exploration_fraction=0.1,
+        exploration_final_eps=0.02,
+        print_freq=10,
+        callback=callback
+    )
+    print("Saving model to kuka_cam_model.pkl")
+    act.save("kuka_cam_model.pkl")
+
+
+if __name__ == '__main__':
+    main()

examples/pybullet/gym/train_pybullet_cartpole.py

@@ -0,0 +1,33 @@
+import gym
+from envs.bullet.cartpole_bullet import CartPoleBulletEnv
+
+from baselines import deepq
+
+
+def callback(lcl, glb):
+    # stop training if reward exceeds 199
+    is_solved = lcl['t'] > 100 and sum(lcl['episode_rewards'][-101:-1]) / 100 >= 199
+    return is_solved
+
+
+def main():
+	
+    env = gym.make('CartPoleBulletEnv-v0')
+    model = deepq.models.mlp([64])
+    act = deepq.learn(
+        env,
+        q_func=model,
+        lr=1e-3,
+        max_timesteps=100000,
+        buffer_size=50000,
+        exploration_fraction=0.1,
+        exploration_final_eps=0.02,
+        print_freq=10,
+        callback=callback
+    )
+    print("Saving model to cartpole_model.pkl")
+    act.save("cartpole_model.pkl")
+
+
+if __name__ == '__main__':
+    main()