diff --git a/legged_gym/legged_gym/envs/a1/a1_leap_config.py b/legged_gym/legged_gym/envs/a1/a1_leap_config.py
index 7963d51..ae6bd5b 100644
--- a/legged_gym/legged_gym/envs/a1/a1_leap_config.py
+++ b/legged_gym/legged_gym/envs/a1/a1_leap_config.py
@@ -29,6 +29,8 @@ class A1LeapCfg( A1FieldCfg ):
             ),
             virtual_terrain= True, # Change this to False for real terrain
             no_perlin_threshold= 0.06,
+            n_obstacles_curriculum = True,
+            n_obstacles_per_track=2,
         ))
 
         TerrainPerlin_kwargs = merge_dict(A1FieldCfg.terrain.TerrainPerlin_kwargs, dict(
@@ -102,7 +104,7 @@ class A1LeapCfgPPO( A1FieldCfgPPO ):
         resume = True
         # load_run = "{Your traind walking model directory}"
         # load_run = "May16_18-12-08_WalkingBase_pEnergySubsteps2e-5_aScale0.5"
-        load_run = "Leap_2m_2500"
+        load_run = "Leap_loss_success"
         # load_run = "May15_21-34-27_Skillleap_pEnergySubsteps-1e-06_virtual"#"May15_17-07-38_WalkingBase_pEnergySubsteps2e-5_aScale0.5"
         # load_run = "{Your virtually trained leap model directory}"
         max_iterations = 20000
diff --git a/legged_gym/legged_gym/envs/base/legged_robot.py b/legged_gym/legged_gym/envs/base/legged_robot.py
index 488f38d..feb95f6 100644
--- a/legged_gym/legged_gym/envs/base/legged_robot.py
+++ b/legged_gym/legged_gym/envs/base/legged_robot.py
@@ -141,6 +141,7 @@ class LeggedRobot(BaseTask):
         self.last_actions[:] = self.actions[:]
         self.last_dof_vel[:] = self.dof_vel[:]
         self.last_root_vel[:] = self.root_states[:, 7:13]
+        self.last_root_pos[:] = self.root_states[:, :3]
         self.last_torques[:] = self.torques[:]
 
         if self.viewer and self.enable_viewer_sync and self.debug_viz:
@@ -600,6 +601,7 @@ class LeggedRobot(BaseTask):
         self.last_dof_vel = torch.zeros_like(self.dof_vel)
         self.last_root_vel = torch.zeros_like(self.root_states[:, 7:13])
         self.last_torques = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
+        self.last_root_pos = torch.zeros_like(self.root_states[:, :3])
         self.commands = torch.zeros(self.num_envs, self.cfg.commands.num_commands, dtype=torch.float, device=self.device, requires_grad=False) # x vel, y vel, yaw vel, heading
         self.commands_scale = torch.tensor([self.obs_scales.lin_vel, self.obs_scales.lin_vel, self.obs_scales.ang_vel], device=self.device, requires_grad=False,) # TODO change this
         self.feet_air_time = torch.zeros(self.num_envs, self.feet_indices.shape[0], dtype=torch.float, device=self.device, requires_grad=False)
@@ -638,6 +640,7 @@ class LeggedRobot(BaseTask):
             self.actions[env_ids] = 0.
         self.last_actions[env_ids] = 0.
         self.last_dof_vel[env_ids] = 0.
+        self.last_root_pos[env_ids] = 0.
         self.last_torques[env_ids] = 0.
         self.feet_air_time[env_ids] = 0.
         self.episode_length_buf[env_ids] = 0
diff --git a/legged_gym/legged_gym/envs/base/legged_robot_field.py b/legged_gym/legged_gym/envs/base/legged_robot_field.py
index be46710..03f5fd8 100644
--- a/legged_gym/legged_gym/envs/base/legged_robot_field.py
+++ b/legged_gym/legged_gym/envs/base/legged_robot_field.py
@@ -1038,7 +1038,7 @@ class LeggedRobotField(LeggedRobot):
         return (1 - torch.exp(-world_vel_error/self.cfg.rewards.tracking_sigma)) * engaging_mask # reverse version of tracking reward
 
     def _reward_lin_pos_x(self):
-        return torch.abs((self.root_states[:, :3] - self.env_origins)[:, 0])
+        return (self.root_states[:, :3] - self.last_root_pos)[:, 0]
     
     ##### Some helper functions that override parent class attributes #####
     @property
diff --git a/legged_gym/legged_gym/scripts/play.py b/legged_gym/legged_gym/scripts/play.py
index 4c92741..2a26b08 100644
--- a/legged_gym/legged_gym/scripts/play.py
+++ b/legged_gym/legged_gym/scripts/play.py
@@ -106,7 +106,7 @@ def play(args):
         # "tilt",
     ]
     env_cfg.terrain.BarrierTrack_kwargs["leap"] = dict(
-            length= (1.3, 1.3),
+            length= (1.0, 1.0),
             depth= (0.4, 0.8),
             height= 0.2,
     )
@@ -114,8 +114,8 @@ def play(args):
     if "one_obstacle_per_track" in env_cfg.terrain.BarrierTrack_kwargs.keys():
         env_cfg.terrain.BarrierTrack_kwargs.pop("one_obstacle_per_track")
     env_cfg.terrain.BarrierTrack_kwargs["n_obstacles_per_track"] = 2# 2
-    env_cfg.commands.ranges.lin_vel_x = [3.0, 3.0] # [1.2, 1.2]
-    env_cfg.terrain.BarrierTrack_kwargs['track_block_length']= 3.
+    env_cfg.commands.ranges.lin_vel_x = [1.5, 1.5] # [1.2, 1.2]
+    env_cfg.terrain.BarrierTrack_kwargs['track_block_length']= 2.0
     if "distill" in args.task:
         env_cfg.commands.ranges.lin_vel_x = [0.0, 0.0]
         env_cfg.commands.ranges.lin_vel_y = [-0., 0.]
diff --git a/legged_gym/legged_gym/scripts/play_vel.py b/legged_gym/legged_gym/scripts/play_vel.py
index e2f16b0..9d2ee46 100644
--- a/legged_gym/legged_gym/scripts/play_vel.py
+++ b/legged_gym/legged_gym/scripts/play_vel.py
@@ -106,14 +106,14 @@ def play(args):
         # "tilt",
     ]
     env_cfg.terrain.BarrierTrack_kwargs["leap"] = dict(
-            length= (1.5, 1.5),
+            length= (0.5, 1),
             depth= (0.4, 0.8),
             height= 0.2,
     )
     
     if "one_obstacle_per_track" in env_cfg.terrain.BarrierTrack_kwargs.keys():
         env_cfg.terrain.BarrierTrack_kwargs.pop("one_obstacle_per_track")
-    env_cfg.terrain.BarrierTrack_kwargs["n_obstacles_per_track"] = 1# 2
+    env_cfg.terrain.BarrierTrack_kwargs["n_obstacles_per_track"] = 2# 2
     env_cfg.commands.ranges.lin_vel_x = [3.0, 3.0] # [1.2, 1.2]
     env_cfg.terrain.BarrierTrack_kwargs['track_block_length']= 3.
     if "distill" in args.task:
diff --git a/legged_gym/legged_gym/utils/terrain/barrier_track.py b/legged_gym/legged_gym/utils/terrain/barrier_track.py
index 9567083..c2eb277 100644
--- a/legged_gym/legged_gym/utils/terrain/barrier_track.py
+++ b/legged_gym/legged_gym/utils/terrain/barrier_track.py
@@ -479,16 +479,31 @@ class BarrierTrack:
         # adding trimesh and heighfields
         if "one_obstacle_per_track" in self.track_kwargs.keys():
             print("Warning: one_obstacle_per_track is deprecated, use n_obstacles_per_track instead.")
-        if self.track_kwargs["randomize_obstacle_order"] and len(self.track_kwargs["options"]) > 0:
-            obstacle_order = np.random.choice(
-                len(self.track_kwargs["options"]),
-                size= self.track_kwargs.get("n_obstacles_per_track", 1),
-                replace= True,
-            )
-        else:
-            obstacle_order = np.arange(len(self.track_kwargs["options"]))
+        
         difficulties = self.get_difficulty(row_idx, col_idx)
         difficulty, virtual_track = difficulties[:2]
+        n_obstacles_curriculum = self.track_kwargs.get("n_obstacles_curriculum", False)
+        if n_obstacles_curriculum:    
+            # n_obstacles_per_track = min(int(difficulty * self.track_kwargs.get("n_obstacles_per_track", 1)) + 1, self.track_kwargs.get("n_obstacles_per_track", 1))
+            n_obstacles_per_track = self.track_kwargs.get("n_obstacles_per_track", 1)
+            obstacle_order = np.random.choice(
+                len(self.track_kwargs["options"]),
+                size= n_obstacles_per_track,
+                replace= True,
+            )
+            # if difficulty > 0.5:
+            #     difficulty = (difficulty - 0.2) / 0.8
+            # else:
+            #     difficulty *= 2
+        else:
+            if self.track_kwargs["randomize_obstacle_order"] and len(self.track_kwargs["options"]) > 0:
+                obstacle_order = np.random.choice(
+                    len(self.track_kwargs["options"]),
+                    size= self.track_kwargs.get("n_obstacles_per_track", 1),
+                    replace= True,
+                )
+            else:
+                obstacle_order = np.arange(len(self.track_kwargs["options"]))
 
         if self.track_kwargs["add_perlin_noise"]:
             TerrainPerlin_kwargs = self.cfg.TerrainPerlin_kwargs
diff --git a/rsl_rl/rsl_rl/algorithms/ppo.py b/rsl_rl/rsl_rl/algorithms/ppo.py
index db11708..aa070a2 100644
--- a/rsl_rl/rsl_rl/algorithms/ppo.py
+++ b/rsl_rl/rsl_rl/algorithms/ppo.py
@@ -235,7 +235,7 @@ class PPO:
         if current_learning_iteration is None:
             vel_loss = 0
         else:
-            vel_loss = torch.square(velocity-2).mean() * np.exp(-0.01 * current_learning_iteration + 125)
+            vel_loss = torch.square(velocity-2).mean() * np.exp(-0.01 * current_learning_iteration + 165)
             vel_loss += torch.square(torch.clamp_max(velocity, 1.) - 1).mean()
 
         return_ = dict(
diff --git a/rsl_rl/rsl_rl/runners/on_policy_runner.py b/rsl_rl/rsl_rl/runners/on_policy_runner.py
index c609309..99c6ba4 100644
--- a/rsl_rl/rsl_rl/runners/on_policy_runner.py
+++ b/rsl_rl/rsl_rl/runners/on_policy_runner.py
@@ -208,6 +208,7 @@ class OnPolicyRunner:
                             'collection_time']:.3f}s, learning {locs['learn_time']:.3f}s)\n"""
                           f"""{'Value function loss:':>{pad}} {locs["losses"]['value_loss']:.4f}\n"""
                           f"""{'Surrogate loss:':>{pad}} {locs["losses"]['surrogate_loss']:.4f}\n"""
+                          f"""{'Velocity loss:':>{pad}} {locs["losses"]['vel_loss']:.4f}\n"""
                           f"""{'Mean action noise std:':>{pad}} {mean_std.item():.2f}\n"""
                           f"""{'Mean reward:':>{pad}} {statistics.mean(locs['rewbuffer']):.2f}\n"""
                           f"""{'Mean episode length:':>{pad}} {statistics.mean(locs['lenbuffer']):.2f}\n"""
@@ -221,7 +222,6 @@ class OnPolicyRunner:
                             'collection_time']:.3f}s, learning {locs['learn_time']:.3f}s)\n"""
                           f"""{'Value function loss:':>{pad}} {locs["losses"]['value_loss']:.4f}\n"""
                           f"""{'Surrogate loss:':>{pad}} {locs["losses"]['surrogate_loss']:.4f}\n"""
-                          f"""{'Velocity loss:':>{pad}} {locs["losses"]['vel_loss']:.4f}\n"""
                           f"""{'Mean action noise std:':>{pad}} {mean_std.item():.2f}\n"""
                         #   f"""{'Mean reward/step:':>{pad}} {locs['mean_reward']:.2f}\n"""
                         #   f"""{'Mean episode length/episode:':>{pad}} {locs['mean_trajectory_length']:.2f}\n"""