config changed

legged_gym/legged_gym/envs/a1/a1_crawl_config.py

@@ -1,5 +1,5 @@
 import numpy as np
-from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
+from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
 from legged_gym.utils.helpers import merge_dict
 
 class A1CrawlCfg( A1FieldCfg ):

legged_gym/legged_gym/envs/a1/a1_down_config.py

@@ -1,6 +1,6 @@
 from os import path as osp
 import numpy as np
-from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
+from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
 from legged_gym.utils.helpers import merge_dict
 
 class A1DownCfg( A1FieldCfg ):

legged_gym/legged_gym/envs/a1/a1_field_config.py

@@ -1,5 +1,4 @@
 import numpy as np
-import os.path as osp
 from legged_gym.envs.a1.a1_config import A1RoughCfg, A1RoughCfgPPO
 
 class A1FieldCfg( A1RoughCfg ):
@@ -60,7 +59,7 @@ class A1FieldCfg( A1RoughCfg ):
 
         BarrierTrack_kwargs = dict(
             options= [
-                # "jump",
+                # "climb",
                 # "crawl",
                 # "tilt",
                 # "leap",
@@ -69,11 +68,11 @@ class A1FieldCfg( A1RoughCfg ):
             track_block_length= 2., # the x-axis distance from the env origin point
             wall_thickness= (0.04, 0.2), # [m]
             wall_height= -0.05,
-            jump= dict(
+            climb= dict(
                 height= (0.2, 0.6),
                 depth= (0.1, 0.8), # size along the forward axis
                 fake_offset= 0.0, # [m] an offset that make the robot easier to get into the obstacle
-                jump_down_prob= 0., # probability of jumping down use it in non-virtual terrain
+                climb_down_prob= 0.0,
             ),
             crawl= dict(
                 height= (0.25, 0.5),
@@ -98,14 +97,13 @@ class A1FieldCfg( A1RoughCfg ):
             virtual_terrain= False,
             draw_virtual_terrain= True,
             engaging_next_threshold= 1.2,
-            engaging_finish_threshold= 0.,
             curriculum_perlin= False,
-            no_perlin_threshold= 0.1,
+            no_perlin_threshold= 0.0,
         )
 
         TerrainPerlin_kwargs = dict(
-            zScale= [0.08, 0.15],
+            zScale= [0.1, 0.15],
-            # zScale= 0.15, # Use a constant zScale for training a walk policy
+            # zScale= 0.1, # Use a constant zScale for training a walk policy
             frequency= 10,
         )
 
@@ -116,6 +114,9 @@ class A1FieldCfg( A1RoughCfg ):
             lin_vel_x = [-1.0, 1.0]
             lin_vel_y = [0.0, 0.0]
             ang_vel_yaw = [0., 0.]
+            ######## configs for training a walk policy #########
+            # lin_vel_y = [-1., 1.]
+            # ang_vel_yaw = [-1., 1.]
 
     class control( A1RoughCfg.control ):
         stiffness = {'joint': 50.}
@@ -124,15 +125,10 @@ class A1FieldCfg( A1RoughCfg ):
         torque_limits = 25 # override the urdf
         computer_clip_torque = True
         motor_clip_torque = False
-        # action_delay = False # set to True to enable action delay in sim
-        # action_delay_range = [0.002, 0.022] # [s]
-        # action_delay_resample_time = 5.0 # [s]
 
     class asset( A1RoughCfg.asset ):
-        penalize_contacts_on = ["base", "thigh", "calf"]
+        penalize_contacts_on = ["base", "thigh"]
         terminate_after_contacts_on = ["base", "imu"]
-        front_hip_names = ["FR_hip_joint", "FL_hip_joint"]
-        rear_hip_names = ["RR_hip_joint", "RL_hip_joint"]
 
     class termination:
         # additional factors that determines whether to terminates the episode
@@ -149,8 +145,8 @@ class A1FieldCfg( A1RoughCfg ):
             tilt_threshold= 1.5,
         )
         pitch_kwargs = dict(
-            threshold= 1.6, # [rad] # for leap, jump
+            threshold= 1.6, # [rad] # for leap, climb
-            jump_threshold= 1.6,
+            climb_threshold= 1.6,
             leap_threshold= 1.5,
         )
         z_low_kwargs = dict(
@@ -180,7 +176,7 @@ class A1FieldCfg( A1RoughCfg ):
         added_mass_range = [1.0, 3.0]
 
         randomize_friction = True
-        friction_range = [0., 2.]
+        friction_range = [0.0, 1.]
 
         init_base_pos_range = dict(
             x= [0.2, 0.6],
@@ -199,34 +195,15 @@ class A1FieldCfg( A1RoughCfg ):
             # penalty for hardware safety
             exceed_dof_pos_limits = -1e-1
             exceed_torque_limits_i = -2e-1
-        soft_dof_pos_limit = 0.01
+        soft_dof_pos_limit = 0.9
 
     class normalization( A1RoughCfg.normalization ):
         class obs_scales( A1RoughCfg.normalization.obs_scales ):
             forward_depth = 1.
-            base_pose = [0., 0., 0., 1., 1., 1.]
+            base_pose = [0., 0., 1., 1., 1., 1.]
             engaging_block = 1.
-        """ The following action clip is used for tanh policy activation. """
-        # clip_actions_method = "hard"
-        # dof_pos_redundancy = 0.2
-        # clip_actions_low = []
-        # clip_actions_high = []
-        # for sdk_joint_name, sim_joint_name in zip(
-        #     ["Hip", "Thigh", "Calf"] * 4,
-        #     [ # in the order as simulation
-        #         "FL_hip_joint", "FL_thigh_joint", "FL_calf_joint",
-        #         "FR_hip_joint", "FR_thigh_joint", "FR_calf_joint",
-        #         "RL_hip_joint", "RL_thigh_joint", "RL_calf_joint",
-        #         "RR_hip_joint", "RR_thigh_joint", "RR_calf_joint",
-        #     ],
-        # ): # This setting is only for position control.
-        #     clip_actions_low.append( (A1RoughCfg.asset.sdk_dof_range[sdk_joint_name + "_min"] + dof_pos_redundancy - A1RoughCfg.init_state.default_joint_angles[sim_joint_name]) / a1_action_scale )
-        #     clip_actions_high.append( (A1RoughCfg.asset.sdk_dof_range[sdk_joint_name + "_max"] - dof_pos_redundancy - A1RoughCfg.init_state.default_joint_angles[sim_joint_name]) / a1_action_scale )
-        # del dof_pos_redundancy, sdk_joint_name, sim_joint_name # This is not intended to be an attribute of normalization
-        """ The above action clip is used for tanh policy activation. """
 
     class noise( A1RoughCfg.noise ):
-        add_noise = False # disable internal uniform +- 1 noise, and no noise in proprioception
         class noise_scales( A1RoughCfg.noise.noise_scales ):
             forward_depth = 0.1
             base_pose = 1.0
@@ -267,7 +244,6 @@ class A1FieldCfg( A1RoughCfg ):
         no_moveup_when_fall = False
         # chosen heuristically, please refer to `LeggedRobotField._get_terrain_curriculum_move` with fixed body_measure_points
 
-logs_root = osp.join(osp.dirname(osp.dirname(osp.dirname(osp.dirname(osp.abspath(__file__))))), "logs")
 class A1FieldCfgPPO( A1RoughCfgPPO ):
     class algorithm( A1RoughCfgPPO.algorithm ):
         entropy_coef = 0.01
@@ -280,9 +256,8 @@ class A1FieldCfgPPO( A1RoughCfgPPO ):
     class runner( A1RoughCfgPPO.runner ):
         policy_class_name = "ActorCriticRecurrent"
         experiment_name = "field_a1"
-        resume = False
+        run_name = "".join(["WalkingBase",
-        
+        ("_pEnergySubsteps" + np.format_float_scientific(-A1FieldCfg.rewards.scales.legs_energy_substeps, precision=1, exp_digits=1, trim="-") if A1FieldCfg.rewards.scales.legs_energy_substeps != 0 else ""),
-        run_name = "".join(["WalkForward",
         ("_propDelay{:.2f}-{:.2f}".format(
                 A1FieldCfg.sensor.proprioception.latency_range[0],
                 A1FieldCfg.sensor.proprioception.latency_range[1],

legged_gym/legged_gym/envs/a1/a1_field_config_new.py

@@ -0,0 +1,302 @@
+import numpy as np
+import os.path as osp
+from legged_gym.envs.a1.a1_config import A1RoughCfg, A1RoughCfgPPO
+
+class A1FieldCfg( A1RoughCfg ):
+    class env( A1RoughCfg.env ):
+        num_envs = 4096 # 8192
+        obs_components = [
+            "proprioception", # 48
+            # "height_measurements", # 187
+            "base_pose",
+            "robot_config",
+            "engaging_block",
+            "sidewall_distance",
+        ]
+        # privileged_use_lin_vel = True # for the possible of setting "proprioception" in obs and privileged obs different
+
+        ######## configs for training a walk policy ############
+        # obs_components = [
+        #     "proprioception", # 48
+        #     # "height_measurements", # 187
+        #     # "forward_depth",
+        #     # "base_pose",
+        #     # "robot_config",
+        #     # "engaging_block",
+        #     # "sidewall_distance",
+        # ]
+        # privileged_obs_components = [
+        #     "proprioception",
+        #     # "height_measurements",
+        #     # "forward_depth",
+        #     "robot_config",
+        # ]
+        ######## End configs for training a walk policy ############
+
+    class sensor:
+        class forward_camera:
+            resolution = [16, 16]
+            position = [0.26, 0., 0.03] # position in base_link
+            rotation = [0., 0., 0.] # ZYX Euler angle in base_link
+    
+        class proprioception:
+            delay_action_obs = False
+            latency_range = [0.0, 0.0]
+            latency_resample_time = 2.0 # [s]
+    
+    class terrain( A1RoughCfg.terrain ):
+        mesh_type = "trimesh" # Don't change
+        num_rows = 20
+        num_cols = 50
+        selected = "BarrierTrack" # "BarrierTrack" or "TerrainPerlin", "TerrainPerlin" can be used for training a walk policy.
+        max_init_terrain_level = 0
+        border_size = 5
+        slope_treshold = 20.
+
+        curriculum = False # for walk
+        horizontal_scale = 0.025 # [m]
+        # vertical_scale = 1. # [m] does not change the value in hightfield
+        pad_unavailable_info = True
+
+        BarrierTrack_kwargs = dict(
+            options= [
+                # "jump",
+                # "crawl",
+                # "tilt",
+                # "leap",
+            ], # each race track will permute all the options
+            track_width= 1.6,
+            track_block_length= 2., # the x-axis distance from the env origin point
+            wall_thickness= (0.04, 0.2), # [m]
+            wall_height= -0.05,
+            jump= dict(
+                height= (0.2, 0.6),
+                depth= (0.1, 0.8), # size along the forward axis
+                fake_offset= 0.0, # [m] an offset that make the robot easier to get into the obstacle
+                jump_down_prob= 0., # probability of jumping down use it in non-virtual terrain
+            ),
+            crawl= dict(
+                height= (0.25, 0.5),
+                depth= (0.1, 0.6), # size along the forward axis
+                wall_height= 0.6,
+                no_perlin_at_obstacle= False,
+            ),
+            tilt= dict(
+                width= (0.24, 0.32),
+                depth= (0.4, 1.), # size along the forward axis
+                opening_angle= 0.0, # [rad] an opening that make the robot easier to get into the obstacle
+                wall_height= 0.5,
+            ),
+            leap= dict(
+                length= (0.2, 1.0),
+                depth= (0.4, 0.8),
+                height= 0.2,
+            ),
+            add_perlin_noise= True,
+            border_perlin_noise= True,
+            border_height= 0.,
+            virtual_terrain= False,
+            draw_virtual_terrain= True,
+            engaging_next_threshold= 1.2,
+            engaging_finish_threshold= 0.,
+            curriculum_perlin= False,
+            no_perlin_threshold= 0.1,
+        )
+
+        TerrainPerlin_kwargs = dict(
+            zScale= [0.08, 0.15],
+            # zScale= 0.15, # Use a constant zScale for training a walk policy
+            frequency= 10,
+        )
+    
+    class commands( A1RoughCfg.commands ):
+        heading_command = False
+        resampling_time = 10 # [s]
+        class ranges( A1RoughCfg.commands.ranges ):
+            lin_vel_x = [-1.0, 1.0]
+            lin_vel_y = [0.0, 0.0]
+            ang_vel_yaw = [0., 0.]
+
+    class control( A1RoughCfg.control ):
+        stiffness = {'joint': 50.}
+        damping = {'joint': 1.}
+        action_scale = 0.5
+        torque_limits = 25 # override the urdf
+        computer_clip_torque = True
+        motor_clip_torque = False
+        # action_delay = False # set to True to enable action delay in sim
+        # action_delay_range = [0.002, 0.022] # [s]
+        # action_delay_resample_time = 5.0 # [s]
+
+    class asset( A1RoughCfg.asset ):
+        penalize_contacts_on = ["base", "thigh", "calf"]
+        terminate_after_contacts_on = ["base", "imu"]
+        front_hip_names = ["FR_hip_joint", "FL_hip_joint"]
+        rear_hip_names = ["RR_hip_joint", "RL_hip_joint"]
+
+    class termination:
+        # additional factors that determines whether to terminates the episode
+        termination_terms = [
+            "roll",
+            "pitch",
+            "z_low",
+            "z_high",
+            # "out_of_track",
+        ]
+
+        roll_kwargs = dict(
+            threshold= 0.8, # [rad]
+            tilt_threshold= 1.5,
+        )
+        pitch_kwargs = dict(
+            threshold= 1.6, # [rad] # for leap, jump
+            jump_threshold= 1.6,
+            leap_threshold= 1.5,
+        )
+        z_low_kwargs = dict(
+            threshold= 0.15, # [m]
+        )
+        z_high_kwargs = dict(
+            threshold= 1.5, # [m]
+        )
+        out_of_track_kwargs = dict(
+            threshold= 1., # [m]
+        )
+
+        check_obstacle_conditioned_threshold = True
+        timeout_at_border = False
+
+    class domain_rand( A1RoughCfg.domain_rand ):
+        randomize_com = True
+        class com_range:
+            x = [-0.05, 0.15]
+            y = [-0.1, 0.1]
+            z = [-0.05, 0.05]
+
+        randomize_motor = True
+        leg_motor_strength_range = [0.9, 1.1]
+
+        randomize_base_mass = True
+        added_mass_range = [1.0, 3.0]
+
+        randomize_friction = True
+        friction_range = [0., 2.]
+
+        init_base_pos_range = dict(
+            x= [0.2, 0.6],
+            y= [-0.25, 0.25],
+        )
+
+        push_robots = False 
+
+    class rewards( A1RoughCfg.rewards ):
+        class scales:
+            tracking_ang_vel = 0.05
+            world_vel_l2norm = -1.
+            legs_energy_substeps = -2e-5
+            legs_energy = -0.
+            alive = 2.
+            # penalty for hardware safety
+            exceed_dof_pos_limits = -1e-1
+            exceed_torque_limits_i = -2e-1
+        soft_dof_pos_limit = 0.01
+
+    class normalization( A1RoughCfg.normalization ):
+        class obs_scales( A1RoughCfg.normalization.obs_scales ):
+            forward_depth = 1.
+            base_pose = [0., 0., 0., 1., 1., 1.]
+            engaging_block = 1.
+        """ The following action clip is used for tanh policy activation. """
+        # clip_actions_method = "hard"
+        # dof_pos_redundancy = 0.2
+        # clip_actions_low = []
+        # clip_actions_high = []
+        # for sdk_joint_name, sim_joint_name in zip(
+        #     ["Hip", "Thigh", "Calf"] * 4,
+        #     [ # in the order as simulation
+        #         "FL_hip_joint", "FL_thigh_joint", "FL_calf_joint",
+        #         "FR_hip_joint", "FR_thigh_joint", "FR_calf_joint",
+        #         "RL_hip_joint", "RL_thigh_joint", "RL_calf_joint",
+        #         "RR_hip_joint", "RR_thigh_joint", "RR_calf_joint",
+        #     ],
+        # ): # This setting is only for position control.
+        #     clip_actions_low.append( (A1RoughCfg.asset.sdk_dof_range[sdk_joint_name + "_min"] + dof_pos_redundancy - A1RoughCfg.init_state.default_joint_angles[sim_joint_name]) / a1_action_scale )
+        #     clip_actions_high.append( (A1RoughCfg.asset.sdk_dof_range[sdk_joint_name + "_max"] - dof_pos_redundancy - A1RoughCfg.init_state.default_joint_angles[sim_joint_name]) / a1_action_scale )
+        # del dof_pos_redundancy, sdk_joint_name, sim_joint_name # This is not intended to be an attribute of normalization
+        """ The above action clip is used for tanh policy activation. """
+
+    class noise( A1RoughCfg.noise ):
+        add_noise = False # disable internal uniform +- 1 noise, and no noise in proprioception
+        class noise_scales( A1RoughCfg.noise.noise_scales ):
+            forward_depth = 0.1
+            base_pose = 1.0
+
+    class viewer( A1RoughCfg.viewer ):
+        pos = [0, 0, 5]  # [m]
+        lookat = [5., 5., 2.]  # [m]
+
+        draw_volume_sample_points = False
+
+    class sim( A1RoughCfg.sim ):
+        body_measure_points = { # transform are related to body frame
+            "base": dict(
+                x= [i for i in np.arange(-0.2, 0.31, 0.03)],
+                y= [-0.08, -0.04, 0.0, 0.04, 0.08],
+                z= [i for i in np.arange(-0.061, 0.061, 0.03)],
+                transform= [0., 0., 0.005, 0., 0., 0.],
+            ),
+            "thigh": dict(
+                x= [
+                    -0.16, -0.158, -0.156, -0.154, -0.152,
+                    -0.15, -0.145, -0.14, -0.135, -0.13, -0.125, -0.12, -0.115, -0.11, -0.105, -0.1, -0.095, -0.09, -0.085, -0.08, -0.075, -0.07, -0.065, -0.05,
+                    0.0, 0.05, 0.1,
+                ],
+                y= [-0.015, -0.01, 0.0, -0.01, 0.015],
+                z= [-0.03, -0.015, 0.0, 0.015],
+                transform= [0., 0., -0.1,   0., 1.57079632679, 0.],
+            ),
+            "calf": dict(
+                x= [i for i in np.arange(-0.13, 0.111, 0.03)],
+                y= [-0.015, 0.0, 0.015],
+                z= [-0.015, 0.0, 0.015],
+                transform= [0., 0., -0.11,   0., 1.57079632679, 0.],
+            ),
+        }
+
+    class curriculum:
+        no_moveup_when_fall = False
+        # chosen heuristically, please refer to `LeggedRobotField._get_terrain_curriculum_move` with fixed body_measure_points
+
+logs_root = osp.join(osp.dirname(osp.dirname(osp.dirname(osp.dirname(osp.abspath(__file__))))), "logs")
+class A1FieldCfgPPO( A1RoughCfgPPO ):
+    class algorithm( A1RoughCfgPPO.algorithm ):
+        entropy_coef = 0.01
+        clip_min_std = 1e-12
+
+    class policy( A1RoughCfgPPO.policy ):
+        rnn_type = 'gru'
+        mu_activation = "tanh"
+    
+    class runner( A1RoughCfgPPO.runner ):
+        policy_class_name = "ActorCriticRecurrent"
+        experiment_name = "field_a1"
+        resume = False
+        
+        run_name = "".join(["WalkForward",
+        ("_propDelay{:.2f}-{:.2f}".format(
+                A1FieldCfg.sensor.proprioception.latency_range[0],
+                A1FieldCfg.sensor.proprioception.latency_range[1],
+            ) if A1FieldCfg.sensor.proprioception.delay_action_obs else ""
+        ),
+        ("_aScale{:d}{:d}{:d}".format(
+                int(A1FieldCfg.control.action_scale[0] * 10),
+                int(A1FieldCfg.control.action_scale[1] * 10),
+                int(A1FieldCfg.control.action_scale[2] * 10),
+            ) if isinstance(A1FieldCfg.control.action_scale, (tuple, list)) \
+            else "_aScale{:.1f}".format(A1FieldCfg.control.action_scale)
+        ),
+        ])
+        resume = False
+        max_iterations = 10000
+        save_interval = 500
+    

legged_gym/legged_gym/envs/a1/a1_field_distill_config.py

@@ -3,7 +3,7 @@ import os
 import os.path as osp
 from datetime import datetime
 import numpy as np
-from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
+from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
 from legged_gym.utils.helpers import merge_dict
 
 class A1FieldDistillCfg( A1FieldCfg ):

legged_gym/legged_gym/envs/a1/a1_jump_config.py

@@ -1,6 +1,6 @@
 from os import path as osp
 import numpy as np
-from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
+from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
 from legged_gym.utils.helpers import merge_dict
 
 class A1JumpCfg( A1FieldCfg ):

legged_gym/legged_gym/envs/a1/a1_leap_config.py

@@ -1,5 +1,5 @@
 import numpy as np
-from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
+from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
 from legged_gym.utils.helpers import merge_dict
 
 class A1LeapCfg( A1FieldCfg ):

legged_gym/legged_gym/envs/a1/a1_remote_config.py

@@ -1,6 +1,6 @@
 import numpy as np
 import os.path as osp
-from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
+from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
 from legged_gym.utils.helpers import merge_dict
 
 class A1RemoteCfg( A1FieldCfg ):

legged_gym/legged_gym/envs/a1/a1_tilt_config.py

@@ -1,5 +1,5 @@
 import numpy as np
-from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
+from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
 from legged_gym.utils.helpers import merge_dict
 
 class A1TiltCfg( A1FieldCfg ):

legged_gym/legged_gym/envs/base/legged_robot.py

@@ -578,7 +578,7 @@ class LeggedRobot(BaseTask):
         self.all_root_states = gymtorch.wrap_tensor(actor_root_state)
         self.root_states = self.all_root_states.view(self.num_envs, -1, 13)[:, 0, :] # (num_envs, 13)
         self.all_dof_states = gymtorch.wrap_tensor(dof_state_tensor)
-        self.dof_state = self.all_dof_states.view(self.num_envs, -1, 2)[:, :self.num_dof, :] # (num_envs, 2)
+        self.dof_state = self.all_dof_states.view(self.num_envs, -1, 2)[:, :self.num_dof, :] # (num_envs, 2) position + velocity
         self.dof_pos = self.dof_state.view(self.num_envs, -1, 2)[..., :self.num_dof, 0]
         self.dof_vel = self.dof_state.view(self.num_envs, -1, 2)[..., :self.num_dof, 1]
         self.base_quat = self.root_states[:, 3:7]

legged_gym/legged_gym/scripts/play.py

@@ -107,8 +107,8 @@ 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
+    env_cfg.terrain.BarrierTrack_kwargs["n_obstacles_per_track"] = 0# 2
-    env_cfg.commands.ranges.lin_vel_x = [1.2, 1.2]
+    env_cfg.commands.ranges.lin_vel_x = [1.2, 1.2] # [1.2, 1.2]
     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.]

legged_gym/legged_gym/utils/terrain/barrier_track.py

@@ -750,7 +750,7 @@ class BarrierTrack:
             block_idx,
             0.,
             (self.n_blocks_per_track - 1),
-        )
+        ).to(int)
 
         # compute whether the robot is still in any of the track
         in_track_mask = (track_idx == track_idx_clipped).all(dim= -1) & (block_idx == block_idx_clipped)