[fix] typo and bugs in loading demonstration for Dagger

* Fix the problem of accessing traj_data before refreshing trajectory handlers
* Also, fix motor_strength when not using LeggedRobotNoisyMixin
* Also, fix barrier_track typo

legged_gym/legged_gym/envs/base/legged_robot.py

@@ -700,6 +700,7 @@ class LeggedRobot(BaseTask):
         Returns:
             [torch.Tensor]: Torques sent to the simulation
         """
+        actions = self.motor_strength * actions
         #pd controller
         if isinstance(self.cfg.control.action_scale, (tuple, list)):
             self.cfg.control.action_scale = torch.tensor(self.cfg.control.action_scale, device= self.sim_device)

legged_gym/legged_gym/scripts/play.py

@@ -103,6 +103,7 @@ def play(args):
             "wave",
         ]
         env_cfg.terrain.BarrierTrack_kwargs["leap"]["fake_offset"] = 0.1
+        env_cfg.terrain.BarrierTrack_kwargs["draw_virtual_terrain"] = True
     else:
         env_cfg.env.num_envs = min(env_cfg.env.num_envs, 1)
         env_cfg.env.episode_length_s = 60
@@ -131,7 +132,7 @@ def play(args):
     env_cfg.viewer.draw_sensors = False
     if hasattr(env_cfg.terrain, "BarrierTrack_kwargs"):
         env_cfg.terrain.BarrierTrack_kwargs["draw_virtual_terrain"] = True
-    # train_cfg.runner.resume = (args.load_run is not None)
+    train_cfg.runner.resume = (args.load_run is not None)
     train_cfg.runner_class_name = "OnPolicyRunner"
     
     if args.no_throw:
@@ -196,6 +197,8 @@ def play(args):
     )
     agent_model = ppo_runner.alg.actor_critic
     policy = ppo_runner.get_inference_policy(device=env.device)
+    if args.sample:
+        policy = agent_model.act
     ### get obs_slice to read the obs
     # obs_slice = get_obs_slice(env.obs_segments, "engaging_block")
     
@@ -205,7 +208,6 @@ def play(args):
         export_policy_as_jit(ppo_runner.alg.actor_critic, path)
         print('Exported policy as jit script to: ', path)
     if RECORD_FRAMES:
-        os.mkdir(os.path.join(LEGGED_GYM_ROOT_DIR, "logs", "images"), exist_ok= True)
         transform = gymapi.Transform()
         transform.p = gymapi.Vec3(*env_cfg.viewer.pos)
         transform.r = gymapi.Quat.from_euler_zyx(0., 0., -np.pi/2)
@@ -214,6 +216,8 @@ def play(args):
             env.envs[0],
             transform= transform,
         )
+        if not os.path.exists(os.path.join(LEGGED_GYM_ROOT_DIR, "logs", args.frames_dir)):
+            os.makedirs(os.path.join(LEGGED_GYM_ROOT_DIR, "logs", args.frames_dir))
 
     logger = Logger(env.dt)
     robot_index = 0 # which robot is used for logging

legged_gym/legged_gym/utils/terrain/barrier_track.py

@@ -1515,7 +1515,7 @@ class BarrierTrack:
         block_idx = torch.floor(forward_distance / self.env_block_length).to(int) # (n,)
         block_idx_clipped = torch.clip(
             block_idx,
-            0.,
+            0,
             (self.n_blocks_per_track - 1),
         )
         
@@ -1680,7 +1680,7 @@ class BarrierTrack:
             dim= -1,
         )
         if mask_only:
-            return distance_to_edge < self.track_kwargs["stairsup"].get("residual_distance", 0.05)
+            return (distance_to_edge < self.track_kwargs["stairsup"].get("residual_distance", 0.05)).to(torch.float32)
         else:
             return torch.clip(
                 self.track_kwargs["stairsup"].get("residual_distance", 0.05) - distance_to_edge,

rsl_rl/rsl_rl/storage/rollout_files/rollout_dataset.py

@@ -126,6 +126,8 @@ class RolloutDataset(RolloutFileBase):
         self.traj_datas = [None for _ in range(self.num_envs)]
         self.traj_cursors = np.zeros(self.num_envs, dtype= int)
 
+        self.refresh_handlers()
+
     def _refresh_traj_data(self, env_idx):
         """ refresh `self.traj_data` based on current traj_file_idxs[env_idx]. usually called
         after refreshing traj_handler or updated traj_file_idxs[env_idx]
@@ -225,7 +227,7 @@ class RolloutDataset(RolloutFileBase):
             )
             dones = torch.empty(
                 leading_dims,
-                dtype= self.traj_datas[0]["dones"].dtype,
+                dtype= bool,
                 device= self.device,
             )
             timeouts = torch.empty(