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docs: fix wording in guides (#3939)

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Generated-by: OpenAI Codex

Signed-off-by: aineoae86-sys <ai.neo.ae86@gmail.com>
Co-authored-by: aineoae86-sys <ai.neo.ae86@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
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2026-07-29 22:24:03 +08:00
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parent 5594eba06a
commit b4e2d0b610
3 changed files with 10 additions and 10 deletions
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docs/source/rtc.mdx
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@@ -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.