v1.0.2-rc1; mv rem_cts to moe_cts, moe_cts to moe_no_goal_cts, fix rem params same as moe, add --robogauge to start

legged_gym/utils/exporter.py

@@ -81,9 +81,9 @@ class _TorchPolicyExporter(torch.nn.Module):
                 self.obs_no_goal_mask = copy.deepcopy(policy.obs_no_goal_mask).cpu()
             self.history_length = policy.history.shape[1]
             self.history = torch.zeros([1, policy.history.shape[1], policy.history.shape[2]], device='cpu')
-            self.forward = self.forward_moe_cts
+            self.forward = self.forward_moe_no_goal_cts
             if not hasattr(policy, "obs_no_goal_mask"):
-                self.forward = self.forward_rem_cts
+                self.forward = self.forward_moe_cts
         if hasattr(policy, "actor_mcp"):
             self.actor = copy.deepcopy(policy.actor_mcp)
             self.obs_no_goal_mask = copy.deepcopy(policy.obs_no_goal_mask).cpu()
@@ -134,7 +134,7 @@ class _TorchPolicyExporter(torch.nn.Module):
         x = torch.cat([latent, x], dim=1)
         return self.actor(x), (None, latent)
     
-    def forward_moe_cts(self, x):  # x is single observations
+    def forward_moe_no_goal_cts(self, x):  # x is single observations
         x = self.normalizer(x)
         self.history = torch.cat([self.history[:, 1:], x.unsqueeze(1)], dim=1)
         history_no_goal = self.history.reshape(1, self.history_length, -1)[:, :, self.obs_no_goal_mask].reshape(1, -1)
@@ -142,7 +142,7 @@ class _TorchPolicyExporter(torch.nn.Module):
         x = torch.cat([latent, x], dim=1)
         return self.actor(x), (weights, latent)
 
-    def forward_rem_cts(self, x):  # x is single observations
+    def forward_moe_cts(self, x):  # x is single observations
         x = self.normalizer(x)
         self.history = torch.cat([self.history[:, 1:], x.unsqueeze(1)], dim=1)
         latent, weights = self.student_moe_encoder(self.history.flatten(1))
@@ -211,12 +211,12 @@ class _OnnxPolicyExporter(torch.nn.Module):
         elif hasattr(policy, "student_moe_encoder"):
             self.student_moe_encoder = copy.deepcopy(policy.student_moe_encoder)
             self.history_length = policy.history.shape[1]
-            self.forward = self.forward_moe_cts
+            self.forward = self.forward_moe_no_goal_cts
             self.input_dim = self.history_length * policy.history.shape[2]
             if hasattr(policy, "obs_no_goal_mask"):
                 self.obs_no_goal_mask = copy.deepcopy(policy.obs_no_goal_mask).cpu()
             else:
-                self.forward = self.forward_rem_cts
+                self.forward = self.forward_moe_cts
         
         else:  # PPO
             self.forward = self.forward_ppo
@@ -277,7 +277,7 @@ class _OnnxPolicyExporter(torch.nn.Module):
         
         return self.actor(x)
 
-    def forward_moe_cts(self, x):
+    def forward_moe_no_goal_cts(self, x):
         x = self.normalizer(x)
         history, obs_dim = self.flatten_obs(x)
 
@@ -290,7 +290,7 @@ class _OnnxPolicyExporter(torch.nn.Module):
 
         return self.actor(x), weights, latent
 
-    def forward_rem_cts(self, x):
+    def forward_moe_cts(self, x):
         x = self.normalizer(x)
         history, obs_dim = self.flatten_obs(x)
 
@@ -319,7 +319,7 @@ class _OnnxPolicyExporter(torch.nn.Module):
         obs = torch.zeros(1, self.input_dim)
         
         output_names = ["actions"]
-        if self.forward == self.forward_moe_cts:
+        if self.forward == self.forward_moe_no_goal_cts:
             output_names.append("weights")
             output_names.append("latent")
         if self.forward == self.forward_mcp_cts:

legged_gym/utils/helpers.py

@@ -118,6 +118,10 @@ def update_cfg_from_args(env_cfg, cfg_train, args):
             cfg_train.runner.load_run = args.load_run
         if args.checkpoint is not None:
             cfg_train.runner.checkpoint = args.checkpoint
+        if args.robogauge is not None:
+            cfg_train.robogauge.enabled = args.robogauge
+        if args.robogauge_port is not None:
+            cfg_train.robogauge.port = args.robogauge_port
 
     return env_cfg, cfg_train
 
@@ -136,6 +140,9 @@ def get_args():
         {"name": "--num_envs", "type": int, "help": "Number of environments to create. Overrides config file if provided."},
         {"name": "--seed", "type": int, "help": "Random seed. Overrides config file if provided."},
         {"name": "--max_iterations", "type": int, "help": "Maximum number of training iterations. Overrides config file if provided."},
+
+        {"name": "--robogauge", "action": "store_true", "default": False, "help": "Enable robogauge evaluation interface."},
+        {"name": "--robogauge_port", "type": int, "default": 9973, "help": "Port for robogauge evaluation interface."},
     ]
     # parse arguments
     args = gymutil.parse_arguments(