fix direction and encoding

examples/rac/rac_data_collection_openarms.py

@@ -275,10 +275,7 @@ def rac_rollout_loop(
         if events["correction_active"]:
             # Human controlling - record correction data with soft gains
             robot_action = teleop.get_action()
-            # Use lower gains for smoother human control
-            soft_kp = {k.removesuffix(".pos"): 60.0 for k in robot_action if k.endswith(".pos")}
-            soft_kd = {k.removesuffix(".pos"): 1.5 for k in robot_action if k.endswith(".pos")}
-            robot.send_action(robot_action, custom_kp=soft_kp, custom_kd=soft_kd)
+            robot.send_action(robot_action)
             stats["correction_frames"] += 1
             
             # Record this frame

src/lerobot/datasets/lerobot_dataset.py

@@ -1202,42 +1202,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         if has_video_keys and not use_batched_encoding:
             video_paths = self._encode_multiple_temporary_episode_videos(self.meta.video_keys, episode_index)
-            for (video_key, video_path) in zip(self.meta.video_keys, video_paths):
+            for video_key, video_path in zip(self.meta.video_keys, video_paths):
                 ep_metadata.update(self._save_episode_video(video_key, episode_index, video_path))
-            num_cameras = len(self.meta.video_keys)
-            if parallel_encoding and num_cameras > 1:
-                # TODO(Steven): Ideally we would like to control the number of threads per encoding such that:
-                # num_cameras * num_threads = (total_cpu -1)
-                with concurrent.futures.ProcessPoolExecutor(max_workers=num_cameras) as executor:
-                    future_to_key = {
-                        executor.submit(
-                            _encode_video_worker,
-                            video_key,
-                            episode_index,
-                            self.root,
-                            self.fps,
-                        ): video_key
-                        for video_key in self.meta.video_keys
-                    }
-
-                    results = {}
-                    for future in concurrent.futures.as_completed(future_to_key):
-                        video_key = future_to_key[future]
-                        try:
-                            temp_path = future.result()
-                            results[video_key] = temp_path
-                        except Exception as exc:
-                            logging.error(f"Video encoding failed for {video_key}: {exc}")
-                            raise exc
-
-                for video_key in self.meta.video_keys:
-                    temp_path = results[video_key]
-                    ep_metadata.update(
-                        self._save_episode_video(video_key, episode_index, temp_path=temp_path)
-                    )
-            else:
-                for video_key in self.meta.video_keys:
-                    ep_metadata.update(self._save_episode_video(video_key, episode_index))
 
         # `meta.save_episode` need to be executed after encoding the videos
         self.meta.save_episode(episode_index, episode_length, episode_tasks, ep_stats, ep_metadata)

src/lerobot/teleoperators/openarms_mini/openarms_mini.py

@@ -293,7 +293,7 @@ class OpenArmsMini(Teleoperator):
         start = time.perf_counter()
         
         # Motors to flip (invert direction) - different for each arm
-        right_motors_to_flip = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5"]
+        right_motors_to_flip = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_7"]
         left_motors_to_flip = ["joint_1", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7"]
         
         # Joint mapping: leader joint 6 -> follower joint 7, leader joint 7 -> follower joint 6
@@ -330,7 +330,7 @@ class OpenArmsMini(Teleoperator):
             feedback: Dictionary with motor positions (e.g., "right_joint_1.pos", "left_joint_2.pos")
         """
         # Motors to flip (invert direction) -> matches get_action()
-        right_motors_to_flip = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5"]
+        right_motors_to_flip = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_7"]
         left_motors_to_flip = ["joint_1", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7"]
         
         # Reverse joint mapping: follower joint 7 -> leader joint 6, follower joint 6 -> leader joint 7
@@ -397,7 +397,7 @@ class OpenArmsMini(Teleoperator):
         
         # Reverse joint mapping for target (follower joint 7 -> leader joint 6)
         joint_remap = {"joint_7": "joint_6", "joint_6": "joint_7"}
-        right_motors_to_flip = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5"]
+        right_motors_to_flip = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_7"]
         left_motors_to_flip = ["joint_1", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7"]
         
         # Parse target and apply remapping/flipping