--- a
+++ b/examples/legacy/webinar.py
@@ -0,0 +1,42 @@
+import os
+from osim.env import OsimEnv
+import operator
+import functools
+
+## Define an environment where we teach an agent to stand still
+# We use a walker model and define the objective to keep the center of mass still
+class StandingEnv(OsimEnv):
+    model_path = os.path.join(os.path.dirname(__file__), '../osim/models/gait9dof18musc.osim')    
+    time_limit = 300
+
+    def reward(self):
+        # Get the current state and the last state
+        state_desc = self.get_state_desc()
+        prev_state_desc = self.get_prev_state_desc()
+        if not prev_state_desc:
+            return 0
+
+        # Penalize movement of the pelvis
+        res = -(prev_state_desc["misc"]["mass_center_pos"][0] - state_desc["misc"]["mass_center_pos"][0])**2\
+              -(prev_state_desc["misc"]["mass_center_pos"][1] - state_desc["misc"]["mass_center_pos"][1])**2
+
+        # Penalize very low position of the pelvis
+        res += -(state_desc["joint_pos"]["ground_pelvis"][2] < 0.8)
+        
+        return res
+
+    ## WARNING: THIS IS SUBOPTIMAL WAY TO CREATE OBSERVATION VECTOR
+    ## IT SHOULD BE DESIGNED CAREFULLY FOR EACH MODEL
+    def all_values(self, d):
+        l = [self.all_values(v) if type(v) == dict else v for v in d.values()]
+        l = sum([v if type(v) == list else [v,] for v in l], [])
+        return l
+
+    def get_observation(self):
+        state_desc = self.get_state_desc()
+        return self.all_values(state_desc)
+
+    def get_observation_space_size(self):
+        return 408
+
+