errors

legged_gym/legged_gym/envs/a1/a1_leap_config.py

@@ -10,7 +10,8 @@ class A1LeapCfg( A1FieldCfg ):
     #         delay_action_obs = True
     #         latency_range = [0.04-0.0025, 0.04+0.0075]
     #### uncomment the above to train non-virtual terrain
-    
+    class env(A1FieldCfg.env):
+        num_envs = 4
     class terrain( A1FieldCfg.terrain ):
         max_init_terrain_level = 2
         border_size = 5
@@ -68,6 +69,7 @@ class A1LeapCfg( A1FieldCfg ):
             exceed_dof_pos_limits = -1e-1
             exceed_torque_limits_i = -2e-1
             # track_predict_vel_l2norm = -1.
+        soft_dof_pos_limit = 0.9
 
     class curriculum( A1FieldCfg.curriculum ):
         penetrate_volume_threshold_harder = 9000
@@ -80,6 +82,8 @@ class A1LeapCfgPPO( A1FieldCfgPPO ):
     class algorithm( A1FieldCfgPPO.algorithm ):
         entropy_coef = 0.0
         clip_min_std = 0.2
+        lin_vel_x = [2.0, 3.0]
+        command_scale = 2.0
     
     class runner( A1FieldCfgPPO.runner ):
         policy_class_name = "ActorCriticRecurrent"

rsl_rl/rsl_rl/algorithms/ppo.py

@@ -77,6 +77,7 @@ class PPO:
         self.velocity_planner = velocity_planner
         self.velocity_optimizer = getattr(optim, optimizer_class_name)(self.velocity_planner.parameters(), lr=learning_rate)
         self.lin_vel_x = kwargs.get('lin_vel_x', None)
+        self.command_scale = kwargs.get('command_scale', 2.0)
 
         # PPO parameters
         self.clip_param = clip_param
@@ -106,11 +107,11 @@ class PPO:
         if self.actor_critic.is_recurrent:
             self.transition.hidden_states = self.actor_critic.get_hidden_states()
         # Compute the actions and values
-        vel_obs = torch.cat([obs[:, :9], obs[:, 12:]], dim=1)
+        vel_obs = torch.cat([obs[..., :9], obs[..., 12:]], dim=-1)
         velocity = self.velocity_planner(vel_obs)
         if self.lin_vel_x is not None:
             velocity = torch.clip(velocity, self.lin_vel_x[0], self.lin_vel_x[1])
-
+        velocity *= self.command_scale
         self.transition.actions = self.actor_critic.act(obs, velocity=velocity)[0].detach()
         self.transition.values = self.actor_critic.evaluate(critic_obs).detach()
         self.transition.actions_log_prob = self.actor_critic.get_actions_log_prob(self.transition.actions).detach()

rsl_rl/rsl_rl/modules/actor_critic_recurrent.py

@@ -75,7 +75,8 @@ class ActorCriticRecurrent(ActorCritic):
 
     def act(self, observations, masks=None, hidden_states=None, velocity=None):
         if velocity is not None:
-            observations[..., 9] = velocity.squeeze()
+            # print(velocity.squeeze())
+            observations[..., 9] = velocity.squeeze(-1)
         # vel_obs = torch.cat([observations[:, :9], observations[:, 12:]], dim=1)
         # velocity = self.velocity_planner(vel_obs)
         # velocity = torch.clip(velocity, self.lin_vel_x[0], self.lin_vel_x[1])