add changes

.gitignore

@@ -173,3 +173,4 @@ outputs/
 
 # Dev folders
 .cache/*
+src/libero

.gitmodules

@@ -1,3 +0,0 @@
-[submodule "Libero"]
-	path = Libero
-	url = https://github.com/Lifelong-Robot-Learning/LIBERO.git

examples/5_train_libero.sh

@@ -1,9 +1,9 @@
 #!/bin/bash
 
 # config
-REPO_ID=physical-intelligence/libero
+REPO_ID=yzembodied/libero_10_image_task_1
 TASK=libero_10
-OUTPUT_DIR=./outputs/train_run/smolvla2_libero
+OUTPUT_DIR=./outputs/
 
 # clean previous run
 rm -rf $OUTPUT_DIR
@@ -11,7 +11,7 @@ rm -rf $OUTPUT_DIR
 # training params
 STEPS=100000
 BATCH_SIZE=4
-EVAL_FREQ=2000
+EVAL_FREQ=1
 SAVE_FREQ=10000
 NUM_WORKERS=0
 
@@ -33,7 +33,8 @@ MAX_IMAGE_DIM=1024
 
 echo -e "\033[1;33m[WARNING]\033[0m LIBERO is not yet fully supported in this PR!"
 # launch
-MUJOCO_GL=egl python src/lerobot/scripts/train.py \
+PYTORCH_ENABLE_MPS_FALLBACK=1 DEVICE=cpu python src/lerobot/scripts/train.py \
+  --policy.device=cpu \
   --policy.type=$POLICY \
   --dataset.repo_id=$REPO_ID \
   --env.type=libero \
@@ -48,12 +49,7 @@ MUJOCO_GL=egl python src/lerobot/scripts/train.py \
   --policy.max_state_dim=$MAX_STATE_DIM \
   --policy.use_amp=$USE_AMP \
   --policy.optimizer_lr=$OPTIMIZER_LR \
-  --policy.peft_method=$PEFT_METHOD \
   --policy.load_vlm_weights=$LOAD_VLM_WEIGHTS \
   --policy.repo_id=$VLM_REPO_ID \
-  --dataset.use_imagenet_stats=$USE_IMAGENET_STATS \
+  --env.multitask_eval=False \
-  --dataset.image_transforms.enable=$ENABLE_IMG_TRANSFORM \
+  --eval.batch_size=1 \
-  --dataset.max_num_images=$MAX_NUM_IMAGES \
-  --dataset.max_image_dim=$MAX_IMAGE_DIM \
-  # --policy.exclude_image_keys=wrist_image \
-  # --policy.use_env_state=false

src/lerobot/constants.py

@@ -19,8 +19,10 @@ from huggingface_hub.constants import HF_HOME
 
 OBS_ENV_STATE = "observation.environment_state"
 OBS_STATE = "observation.state"
-OBS_IMAGE = "observation.image"
+# OBS_IMAGE = "observation.image"
-OBS_IMAGE_2 = "observation.image2"
+# OBS_IMAGE_2 = "observation.image2"
+OBS_IMAGE = "image"
+OBS_IMAGE_2 = "wrist_image"
 OBS_IMAGES = "observation.images"
 ACTION = "action"
 REWARD = "next.reward"

src/lerobot/envs/libero.py

@@ -174,10 +174,12 @@ class LiberoEnv(gym.Env):
         self.camera_name = camera_name.split(
             ","
         )  # agentview_image (main) or robot0_eye_in_hand_image (wrist)
+        #TODO: jadechoghari, check mapping
         self.camera_name_mapping = {
             "agentview_image": OBS_IMAGE,
             "robot0_eye_in_hand_image": OBS_IMAGE_2,
         }
+
         self.num_steps_wait = (
             10  # Do nothing for the first few timesteps to wait for the simulator drops objects
         )
@@ -220,7 +222,7 @@ class LiberoEnv(gym.Env):
                     "agent_pos": spaces.Box(
                         low=-1000.0,
                         high=1000.0,
-                        shape=(8,),
+                        shape=(8,), #TODO: jadechoghari, check compatible
                         dtype=np.float64,
                     ),
                 }
@@ -293,7 +295,7 @@ class LiberoEnv(gym.Env):
         info = {"is_success": False}
         return observation, info
 
-    def step(self, action):
+    def step1(self, action):
         assert action.ndim == 1
         raw_obs, reward, done, info = self._env.step(action)
 
@@ -307,6 +309,18 @@ class LiberoEnv(gym.Env):
             print(self.task, self.task_id, done, is_success)
         truncated = False
         return observation, reward, terminated, truncated, info
+    def step(self, action):
+        assert action.ndim == 1
+        raw_obs, reward, done, info = self._env.step(action)
+
+        is_success = self._env.check_success()
+        terminated = done or is_success
+        info["is_success"] = is_success
+        print(f"[LiberoEnv.step] done={done}, is_success={is_success}, terminated={terminated}")
+        observation = self._format_raw_obs(raw_obs)
+        truncated = False
+        # note if it is unable to complete get libero error after many steps
+        return observation, reward, terminated, truncated, info
 
     def close(self):
         self._env.close()

src/lerobot/scripts/eval.py

@@ -143,7 +143,7 @@ def rollout(
         leave=False,
     )
     check_env_attributes_and_types(env)
-    while not np.all(done):
+    while not np.all(done) and step < max_steps:
         # Numpy array to tensor and changing dictionary keys to LeRobot policy format.
         observation = preprocess_observation(observation)
         if return_observations:
@@ -185,8 +185,11 @@ def rollout(
         all_successes.append(torch.tensor(successes))
 
         step += 1
+        print(step)
         running_success_rate = (
-            einops.reduce(torch.stack(all_successes, dim=1), "b n -> b", "any").numpy().mean()
+            # einops.reduce(torch.stack(all_successes, dim=1), "b n -> b", "any").numpy().mean() #TODO: changed by jade
+            einops.reduce(torch.stack(all_successes, dim=1), "b n -> b", "max")
+
         )
         progbar.set_postfix({"running_success_rate": f"{running_success_rate.item() * 100:.1f}%"})
         progbar.update()
@@ -315,7 +318,8 @@ def eval_policy(
         sum_rewards.extend(batch_sum_rewards.tolist())
         batch_max_rewards = einops.reduce((rollout_data["reward"] * mask), "b n -> b", "max")
         max_rewards.extend(batch_max_rewards.tolist())
-        batch_successes = einops.reduce((rollout_data["success"] * mask), "b n -> b", "any")
+        # batch_successes = einops.reduce((rollout_data["success"] * mask), "b n -> b", "any")
+        batch_successes = einops.reduce((rollout_data["success"] * mask).float(), "b n -> b", "max")
         all_successes.extend(batch_successes.tolist())
         if seeds:
             all_seeds.extend(seeds)