Make it compatible with LeRobot

README.md

@@ -16,12 +16,13 @@ pip install gym-pusht
 import gymnasium as gym
 import gym_pusht
 
-env = gym.make("gym_pusht/PushT-v0", render_mode="human")
+env = gym.make("gym_pusht/PushT-v0", render_mode="human", render_size=(84,84), visualization_size=(680,680))
 observation, info = env.reset()
 
 for _ in range(1000):
     action = env.action_space.sample()
     observation, reward, terminated, truncated, info = env.step(action)
+    image = env.render()
 
     if terminated or truncated:
         observation, info = env.reset()

gym_pusht/envs/pusht.py

@@ -133,6 +133,14 @@ def __init__(
             )
         elif self.obs_type == "pixels":
             self.observation_space = spaces.Box(low=0, high=255, shape=(render_size, render_size, 3), dtype=np.uint8)
+        elif self.obs_type == "pixels_agent_pos":
+            self.observation_space = spaces.Dict({
+                "pixels": spaces.Box(low=0, high=255, shape=(render_size, render_size, 3), dtype=np.uint8),
+                "agent_pos": spaces.Box(
+                    low=np.array([0, 0], dtype=np.float32),
+                    high=np.array([512, 512], dtype=np.float32),
+                ),
+            })
 
         self.action_space = spaces.Box(low=0, high=512, shape=(2,), dtype=np.float32)
 
@@ -186,14 +194,14 @@ def step(self, action):
         coverage = intersection_area / goal_area
         reward = np.clip(coverage / self.success_threshold, 0.0, 1.0)
         is_success = coverage > self.success_threshold
+        terminated = is_success
 
         observation = self._get_obs()
         info = self._get_info()
-
         info["is_success"] = is_success
-        terminated = is_success
 
-        return observation, reward, terminated, False, info
+        truncated = False
+        return observation, reward, terminated, truncated, info
 
     def reset(self, seed=None, options=None):
         super().reset(seed=seed)
@@ -202,11 +210,22 @@ def reset(self, seed=None, options=None):
         if options is not None and options.get("reset_to_state") is not None:
             state = np.array(options.get("reset_to_state"))
         else:
-            state = self.np_random.uniform(low=[50, 50, 100, 100, -np.pi], high=[450, 450, 400, 400, np.pi])
+            #state = self.np_random.uniform(low=[50, 50, 100, 100, -np.pi], high=[450, 450, 400, 400, np.pi])
+            rs = np.random.RandomState(seed=seed)
+            state = np.array(
+                [
+                    rs.randint(50, 450),
+                    rs.randint(50, 450),
+                    rs.randint(100, 400),
+                    rs.randint(100, 400),
+                    rs.randn() * 2 * np.pi - np.pi,
+                ]
+            )
         self._set_state(state)
 
         observation = self._get_obs()
         info = self._get_info()
+        info["is_success"] = False
 
         if self.render_mode == "human":
             self.render()
@@ -296,6 +315,12 @@ def _get_obs(self):
         elif self.obs_type == "pixels":
             screen = self._draw()
             obs = self._get_img(screen)
+        elif self.obs_type == "pixels_agent_pos":
+            screen = self._draw()
+            obs = {
+                "pixels": self._get_img(screen),
+                "agent_pos": np.array(self.agent.position),
+            }
         return obs
 
     def _get_goal_pose_body(self, pose):