From b4e2d0b61017a0db646a12c98a2df3837e37a1b9 Mon Sep 17 00:00:00 2001 From: Old-Ding <35417409+Old-Ding@users.noreply.github.com> Date: Wed, 29 Jul 2026 22:24:03 +0800 Subject: [PATCH] docs: fix wording in guides (#3939) Generated-by: OpenAI Codex Signed-off-by: aineoae86-sys Co-authored-by: aineoae86-sys Co-authored-by: Steven Palma --- docs/source/feetech.mdx | 8 ++++---- docs/source/processors_robots_teleop.mdx | 8 ++++---- docs/source/rtc.mdx | 4 ++-- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/source/feetech.mdx b/docs/source/feetech.mdx index bba60e4cc..8012d62bc 100644 --- a/docs/source/feetech.mdx +++ b/docs/source/feetech.mdx @@ -40,10 +40,10 @@ This tutorial guides you through updating the firmware of Feetech motors using t For each motor you want to update: 1. **Select the motor** from the list by clicking on it -2. **Click on Upgrade tab**: -3. **Click on Online button**: - - If an potential firmware update is found, it will be displayed in the box -4. **Click on Upgrade button**: +2. **Click the Upgrade tab**: +3. **Click the Online button**: + - If a potential firmware update is found, it will be displayed in the box +4. **Click the Upgrade button**: - The update progress will be displayed ## Step 6: Verify Update diff --git a/docs/source/processors_robots_teleop.mdx b/docs/source/processors_robots_teleop.mdx index 093a8e0e3..359b37aff 100644 --- a/docs/source/processors_robots_teleop.mdx +++ b/docs/source/processors_robots_teleop.mdx @@ -22,7 +22,7 @@ With processors, you choose the learning features you want to use for your polic ## Three pipelines We often compose three pipelines. Depending on your setup, some can be empty if action and observation spaces already match. -Each of these pipelines handle different conversions between different action and observation spaces. Below is a quick explanation of each pipeline. +Each of these pipelines handles different conversions between different action and observation spaces. Below is a quick explanation of each pipeline. 1. Pipeline 1: Teleop action space → dataset action space (phone pose → EE targets) 2. Pipeline 2: Dataset action space → robot command space (EE targets → joints) @@ -74,15 +74,15 @@ In the phone to SO-100 follower examples we use the following adapters: - `robot_action_to_transition`: transforms the teleop action dict to a pipeline transition. - `transition_to_robot_action`: transforms the pipeline transition to a robot action dict. - `observation_to_transition`: transforms the robot observation dict to a pipeline transition. -- `transition_to_observation`: transforms the pipeline transition to a observation dict. +- `transition_to_observation`: transforms the pipeline transition to an observation dict. -Checkout [src/lerobot/processor/converters.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/converters.py) for more details. +Check out [src/lerobot/processor/converters.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/converters.py) for more details. ## Dataset feature contracts Dataset features are determined by the keys saved in the dataset. Each step can declare what features it modifies in a contract called `transform_features(...)`. Once you build a processor, the processor can then aggregate all of these features with `aggregate_pipeline_dataset_features()` and merge multiple feature dicts with `combine_feature_dicts(...)`. -Below is and example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples: +Below is an example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples: ```python def transform_features( diff --git a/docs/source/rtc.mdx b/docs/source/rtc.mdx index eadc34344..7ba136367 100644 --- a/docs/source/rtc.mdx +++ b/docs/source/rtc.mdx @@ -57,7 +57,7 @@ policy_cfg.rtc_config = RTCConfig( policy = PI0Policy.from_pretrained("lerobot/pi0_base", policy_cfg=policy_cfg, device="cuda") # Now use predict_action_chunk with RTC parameters -inference_delay = 4 # How many steps of inference latency, this values should be calculated based on the inference latency of the policy +inference_delay = 4 # How many steps of inference latency, this value should be calculated based on the inference latency of the policy # Initialize the action queue action_queue = ActionQueue(policy_cfg.rtc_config) @@ -100,7 +100,7 @@ Typical values: 8-12 steps RTCConfig(execution_horizon=10) ``` -**`max_guidance_weight`**: How strongly to enforce consistency with the previous chunk. This is a hyperparameter that can be tuned to balance the smoothness of the transitions and the reactivity of the policy. For 10 steps flow matching (SmolVLA, Pi0, Pi0.5), a value of 10.0 is a optimal value. +**`max_guidance_weight`**: How strongly to enforce consistency with the previous chunk. This is a hyperparameter that can be tuned to balance the smoothness of the transitions and the reactivity of the policy. For 10 steps flow matching (SmolVLA, Pi0, Pi0.5), a value of 10.0 is an optimal value. **`prefix_attention_schedule`**: How to weight consistency across the overlap region.