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Author	SHA1	Message	Date
githubnemo	e670ac5daf	Add basic PEFT support to train script + record module (#1411 ) * Add basic support for PEFT adapter methods This changes adds support for training policies with much less parameters by applying adapter methods such as LoRA on specific parts of the policies and therefore possibly higher learning rates / batch sizes. To make this as accessible as possible I thought it useful to provide defaults for `target_modules` and `modules_to_save`. Currently only SmolVLA has such defaults but when we agree that this change is useful I will set out to generate more such defaults. While the user can override these settings, they are expected to only change the peft_method, rank and init_type parameters. * Implement loading of PEFT adapters Loading a PEFT adapter is currently done by initializing a policy with default config and then applying the adapter on the resulting model. This has the obvious drawback that any configurations done during training are not applied in the adapted model. Currently the `use_peft` attribute of `PreTrainedConfig` is only set during loading to signal the following code that it has to deal with a PEFT adapter. However we could imagine a scenario where this is already set at training time and stored alongside the adapter. * Store policy config alongside PEFT checkpoint Before this change the PEFT-wrapped policy did not save the policy's config alongside the adapter config / weights which prevented us from changing the policy config. Now the policy config is saved both in full training and PEFT training. This change makes loading the PEFT policy adapter much easier as well. * Add default config for ACT * Support targets like `all-linear` * Formatting * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix failing tests * Remove PEFT compatibility changes in config We'll wait for the PEFT release that fixes this for good. * Remove `use_peft` parameter from training script Instead we make the PEFT config optional which has the same effect. * Log adapter config to WandB * Better documentation for CLI arguments * Don't unload & merge the PEFT model This can make things hard when using quantized layers (user expects quantized base layers with unquantized adapters for example, merging defaults to upcast the layers leading to higher memory). * Correct way of identifying when to save config * Add CLI end-to-end tests Currently there don't seem to be any way to test the CLI commands. Since this change mostly happens in those I thought it best to add a way to test these commands end-to-end. More integrated commands like `lerobot-record` need patching but standalone commands like training seem to work fine. * Update default targets Removed ACT since it doesn't make sense to fine-tune ACT without having it pretrained beforehand. SmolVLA and Pi0/0.5 are much more senseful targets. * Clean up loading code - Centralized instantiation of the PEFT wrapper in `make_policy` for inference (e.g. in `lerobot-record`) - Training a PEFT policy also sets `cfg.use_peft` so that all inference code loading the policy can rely on that attribute to identify if PEFT loading is needed - Modified RTC example to also include PEFT policies. Mostly because this is an example I'm currently exploring. * Make sure push_to_hub works Since PEFT only wraps `push_to_hub` and not `push_model_to_hub`, the reference to `self` in `policy.push_model_to_hub` is the unwrapped policy which, of course, doesn't know anything about PEFT. To make the upload process aware of PEFT, we pass the unwrapped policy down to `push_model_to_hub` as a kwarg. This is not ideal but I think it is the best way for now. * formatting * Warn when encountering from-scratch-training * Revamp pretrained model loading There were quite a few factors that convinced me that the status quo is able to load pretrained models from the PEFT adapter config but in fact that didn't work. This commit fixes the following things: - policies wrapped in PEFT will now have a `name_or_path` attribute containing the name or path of the pretrained model we're fine-tuning - we further assume that SmolVLA without `pretrained_path` and `load_vlm_weights==False` must be an user-side error - we assume that using PEFT on from-scratch-policies must be an user-side-error * Make it possible to unset policy features This is necessary to train pre-trained policies on new datasets so that the features are inferred from the new dataset and not from the pretrained policy. * Use correct loading for PEFT in RTC example * Make it possible to use PeftModels in eval * Add test checking that PEFT actually reduces params * Adapt state/action projections instead of full-finetuning There doesn't seem to be a benefit to fully fine-tune these layers over just adapting them, so we do that instead. * Disallow PEFT training on non-pretrained policies At first I thought it would make sense to have this feature in case you want to fine-tune a pre-trained section but in the end it makes more trouble than it's worth. It's still possible to allow this in the future when a concrete need arises. * Add basic documentation * Formatting * Add peft as extra dependency, mark tests Fast tests currently fail because of the missing dependency. * Fix pre-commit issues * Add walx <> peft conflict for uv * Exclude peft from pi install for now --------- Co-authored-by: nemo <git@ningu.net> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>	2026-01-05 08:51:26 +01:00
Eugene Mironov	8a915c6b6f	[RTC] Real Time Chunking for Pi0, Smolvla, Pi0.5 (#1698 ) * Add Real-Time Chunking (RTC) support for flow matching models Implement Real-Time Chunking (RTC) for action chunking policies using flow matching denoising. RTC enables smooth action transitions between consecutive chunks by using prefix guidance during denoising. Key features: - RTCProcessor class with denoise_step method for RTC guidance - Tracker system for debug tracking using time-based dictionary storage - RTCDebugVisualizer with comprehensive visualization utilities - Integration with SmolVLA policy for flow matching models - Support for multiple prefix attention schedules (ZEROS, ONES, LINEAR, EXP) - Configurable execution horizon and max guidance weight - Example scripts for dataset evaluation and real-time control Technical details: - Uses autograd-based gradient computation for RTC corrections - Time-based tracking eliminates duplicate step issues - Proxy methods in RTCProcessor for cleaner API - Full integration with LeRobot's policy and dataset systems Files added/modified: - src/lerobot/configs/types.py: Add RTCAttentionSchedule enum - src/lerobot/policies/rtc/: Core RTC implementation - configuration_rtc.py: RTC configuration - modeling_rtc.py: RTCProcessor with denoise_step - debug_handler.py: Tracker for debug information - debug_visualizer.py: Visualization utilities - src/lerobot/policies/smolvla/modeling_smolvla.py: RTC integration - examples/rtc/: Example scripts and evaluation tools 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com> * Fix rtc_config attribute access in SmolVLA Use getattr() to safely check for rtc_config attribute existence instead of direct attribute access. This fixes AttributeError when loading policies without rtc_config in their config. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com> * fixup! Fix rtc_config attribute access in SmolVLA * Add RTCConfig field to SmolVLAConfig Add rtc_config as an optional field in SmolVLAConfig to properly support Real-Time Chunking configuration. This replaces the previous getattr() workarounds with direct attribute access, making the code cleaner and more maintainable. Changes: - Import RTCConfig in configuration_smolvla.py - Add rtc_config: RTCConfig \| None = None field - Revert getattr() calls to direct attribute access in modeling_smolvla.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com> * Refactor RTC enabled checks to use _rtc_enabled helper Add _rtc_enabled() helper method in VLAFlowMatching class to simplify and clean up RTC enabled checks throughout the code. This reduces code duplication and improves readability. Changes: - Add _rtc_enabled() method in VLAFlowMatching - Replace verbose rtc_config checks with _rtc_enabled() calls - Maintain exact same functionality with cleaner code 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com> * Rename track_debug method to track Simplify the method name from track_debug to just track for better readability and consistency. The method already has clear documentation about its debug tracking purpose. Changes: - Rename RTCProcessor.track_debug() to track() - Update all call sites in modeling_smolvla.py and modeling_rtc.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com> * Use output_dir for saving all evaluation images Update eval_dataset.py to save all comparison images to the configured output_dir instead of the current directory. This provides better organization and allows users to specify where outputs should be saved. Changes: - Add os import at top level - Create output_dir at start of run_evaluation() - Save all comparison images to output_dir - Remove duplicate os imports - Update init_rtc_processor() docstring to be more concise 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com> * fixup! Use output_dir for saving all evaluation images * Fix logging buffering and enable tracking when RTC config provided - Add force=True to logging.basicConfig to override existing configuration - Enable line buffering for stdout/stderr for real-time log output - Modify init_rtc_processor to create processor when rtc_config exists even if RTC is disabled, allowing tracking of denoising data 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> * Refactor SmolVLA plotting to use tracker data instead of local variables Remove local tracking variables (correction, x1_t, error) from the denoising loop and instead retrieve plotting data from the RTC tracker after each denoise step. This makes the code cleaner and uses the tracker as the single source of truth for debug/visualization data. Changes: - Remove initialization of correction, x1_t, error before denoising loop - After each Euler step, retrieve most recent debug step from tracker - Extract correction, x1_t, err from debug step for plotting - Update tracking condition to use is_debug_enabled() method 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> * Move plotting logic from modeling_smolvla to eval_dataset script Refactor to improve separation of concerns: modeling_smolvla.py changes: - Remove all plotting logic from sample_actions method - Remove viz_xt_axs, viz_vt_axs, viz_x1t_axs parameters - Remove matplotlib and RTCDebugVisualizer imports - Remove viz_fig, viz_axs, denoise_step_counter instance variables - Simplify denoising loop to only track data in rtc_processor eval_dataset.py changes: - Add _plot_denoising_steps_from_tracker helper method - Retrieve debug steps from tracker after inference - Plot x_t, v_t, x1_t, correction, and error from tracker data - Enable debug tracking (cfg.rtc.debug = True) for visualization - Remove viz axes parameters from predict_action_chunk calls modeling_rtc.py changes: - Remove v_t from track() call (handled by user change) Benefits: - Cleaner modeling code focused on inference - Evaluation script owns all visualization logic - Better separation of concerns - Tracker is single source of truth for debug data 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> * Refactor plotting loging * fixup! Refactor plotting loging * Improve visualization: separate correction plot and fix axis scaling Changes: - Create separate figure for correction data instead of overlaying on v_t - Add _rescale_axes helper method to properly scale all axes - Add 10% margin to y-axis for better visualization - Fix v_t chart vertical compression issue Benefits: - Clearer v_t plot without correction overlay - Better axis scaling with proper margins - Separate correction figure for focused analysis - Improved readability of all denoising visualizations Output files: - denoising_xt_comparison.png (x_t trajectories) - denoising_vt_comparison.png (v_t velocity - now cleaner) - denoising_correction_comparison.png (NEW - separate corrections) - denoising_x1t_comparison.png (x1_t state with error) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com> * fixup! Improve visualization: separate correction plot and fix axis scaling * fixup! fixup! Improve visualization: separate correction plot and fix axis scaling * fixup! fixup! fixup! Improve visualization: separate correction plot and fix axis scaling * Fix traacking * Right kwargs for the policy * Add tests for tracker * Fix tests * Drop not required methods * Add torch compilation for eval_dataset * delete policies * Add matplotliv to dev * fixup! Add matplotliv to dev * Experiemnt with late detach * Debug * Fix compilation * Add RTC to PI0 * Pi0 * Pi0 eval dataset * fixup! Pi0 eval dataset * Turn off compilation for pi0/pi05 * fixup! Turn off compilation for pi0/pi05 * fixup! fixup! Turn off compilation for pi0/pi05 * fixup! fixup! fixup! Turn off compilation for pi0/pi05 * fixup! fixup! fixup! fixup! Turn off compilation for pi0/pi05 * fixup! fixup! fixup! fixup! fixup! Turn off compilation for pi0/pi05 * Add workable flow * Small fixes * Add more tests * Add validatio at the end * Update README * Silent validation * Fix tests * Add tests for modeling_rtc * Add tests for flow matching models with RTC * fixup! Add tests for flow matching models with RTC * fixup! fixup! Add tests for flow matching models with RTC * Add one more test * fixup! Add one more test * Fix test to use _rtc_enabled() instead of is_rtc_enabled() 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fixup! Fix test to use _rtc_enabled() instead of is_rtc_enabled() * fixup! fixup! Fix test to use _rtc_enabled() instead of is_rtc_enabled() * Add RTC initialization tests without config for PI0.5 and SmolVLA Add test_pi05_rtc_initialization_without_rtc_config and test_smolvla_rtc_initialization_without_rtc_config to verify that policies can initialize without RTC config and that _rtc_enabled() returns False in this case. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * Fix PI0.5 init_rtc_processor to use getattr instead of direct model access 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * Fix SmolVLA init_rtc_processor to use getattr instead of direct model access 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * Fix PI0.5 RTC tests to use quantile stats (q01, q99) for normalization 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fixup! Fix PI0.5 RTC tests to use quantile stats (q01, q99) for normalization * Fixup eval with real robot * fixup! Fixup eval with real robot * fixup! fixup! Fixup eval with real robot * Extract simulator logic from eval_with real robot and add proper headers to files * Update images * Fix tests * fixup! Fix tests * add docs for rtc * enhance doc and add images * Fix instal instructions --------- Co-authored-by: Ben Zhang <benzhangniu@gmail.com> Co-authored-by: Alexander Soare <alexander.soare159@gmail.com> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>	2025-11-19 11:19:48 +01:00

Author

SHA1

Message

Date

githubnemo

e670ac5daf

Add basic PEFT support to train script + record module (#1411 )

* Add basic support for PEFT adapter methods

This changes adds support for training policies with much less parameters
by applying adapter methods such as LoRA on specific parts of the policies
and therefore possibly higher learning rates / batch sizes.

To make this as accessible as possible I thought it useful to provide
defaults for `target_modules` and `modules_to_save`. Currently only SmolVLA
has such defaults but when we agree that this change is useful I will set
out to generate more such defaults. While the user can override these
settings, they are expected to only change the peft_method, rank and init_type
parameters.

* Implement loading of PEFT adapters

Loading a PEFT adapter is currently done by initializing a policy with default config
and then applying the adapter on the resulting model. This has the obvious drawback
that any configurations done during training are not applied in the adapted model.

Currently the `use_peft` attribute of `PreTrainedConfig` is only set during loading
to signal the following code that it has to deal with a PEFT adapter. However
we could imagine a scenario where this is already set at training time and stored
alongside the adapter.

* Store policy config alongside PEFT checkpoint

Before this change the PEFT-wrapped policy did not save the policy's config
alongside the adapter config / weights which prevented us from changing the
policy config. Now the policy config is saved both in full training and PEFT
training.

This change makes loading the PEFT policy adapter much easier as well.

* Add default config for ACT

* Support targets like `all-linear`

* Formatting

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Fix failing tests

* Remove PEFT compatibility changes in config

We'll wait for the PEFT release that fixes this for good.

* Remove `use_peft` parameter from training script

Instead we make the PEFT config optional which has the same effect.

* Log adapter config to WandB

* Better documentation for CLI arguments

* Don't unload & merge the PEFT model

This can make things hard when using quantized layers (user expects quantized base layers with
unquantized adapters for example, merging defaults to upcast the layers leading to higher
memory).

* Correct way of identifying when to save config

* Add CLI end-to-end tests

Currently there don't seem to be any way to test the CLI commands.
Since this change mostly happens in those I thought it best to add
a way to test these commands end-to-end.

More integrated commands like `lerobot-record` need patching but
standalone commands like training seem to work fine.

* Update default targets

Removed ACT since it doesn't make sense to fine-tune ACT without having it pretrained beforehand.
SmolVLA and Pi0/0.5 are much more senseful targets.

* Clean up loading code

- Centralized instantiation of the PEFT wrapper in `make_policy` for inference
(e.g. in `lerobot-record`)
- Training a PEFT policy also sets `cfg.use_peft` so that all inference code loading
the policy can rely on that attribute to identify if PEFT loading is needed
- Modified RTC example to also include PEFT policies. Mostly because this is an example
I'm currently exploring.

* Make sure push_to_hub works

Since PEFT only wraps `push_to_hub` and not `push_model_to_hub`, the reference
to `self` in `policy.push_model_to_hub` is the unwrapped policy which, of course,
doesn't know anything about PEFT.

To make the upload process aware of PEFT, we pass the unwrapped policy down to
`push_model_to_hub` as a kwarg. This is not ideal but I think it is the best way
for now.

* formatting

* Warn when encountering from-scratch-training

* Revamp pretrained model loading

There were quite a few factors that convinced me that the status quo
is able to load pretrained models from the PEFT adapter config but
in fact that didn't work.

This commit fixes the following things:
- policies wrapped in PEFT will now have a `name_or_path` attribute
containing the name or path of the pretrained model we're fine-tuning
- we further assume that SmolVLA without `pretrained_path` and
`load_vlm_weights==False` must be an user-side error
- we assume that using PEFT on from-scratch-policies must be
an user-side-error

* Make it possible to unset policy features

This is necessary to train pre-trained policies on new datasets so that the
features are inferred from the new dataset and not from the pretrained
policy.

* Use correct loading for PEFT in RTC example

* Make it possible to use PeftModels in eval

* Add test checking that PEFT actually reduces params

* Adapt state/action projections instead of full-finetuning

There doesn't seem to be a benefit to fully fine-tune these layers
over just adapting them, so we do that instead.

* Disallow PEFT training on non-pretrained policies

At first I thought it would make sense to have this feature
in case you want to fine-tune a pre-trained section but in the
end it makes more trouble than it's worth.

It's still possible to allow this in the future when a concrete
need arises.

* Add basic documentation

* Formatting

* Add peft as extra dependency, mark tests

Fast tests currently fail because of the missing dependency.

* Fix pre-commit issues

* Add walx <> peft conflict for uv

* Exclude peft from pi install for now

---------

Co-authored-by: nemo <git@ningu.net>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>

2026-01-05 08:51:26 +01:00

Eugene Mironov

8a915c6b6f

[RTC] Real Time Chunking for Pi0, Smolvla, Pi0.5 (#1698 )

* Add Real-Time Chunking (RTC) support for flow matching models

Implement Real-Time Chunking (RTC) for action chunking policies using flow
matching denoising. RTC enables smooth action transitions between consecutive
chunks by using prefix guidance during denoising.

Key features:
- RTCProcessor class with denoise_step method for RTC guidance
- Tracker system for debug tracking using time-based dictionary storage
- RTCDebugVisualizer with comprehensive visualization utilities
- Integration with SmolVLA policy for flow matching models
- Support for multiple prefix attention schedules (ZEROS, ONES, LINEAR, EXP)
- Configurable execution horizon and max guidance weight
- Example scripts for dataset evaluation and real-time control

Technical details:
- Uses autograd-based gradient computation for RTC corrections
- Time-based tracking eliminates duplicate step issues
- Proxy methods in RTCProcessor for cleaner API
- Full integration with LeRobot's policy and dataset systems

Files added/modified:
- src/lerobot/configs/types.py: Add RTCAttentionSchedule enum
- src/lerobot/policies/rtc/: Core RTC implementation
  - configuration_rtc.py: RTC configuration
  - modeling_rtc.py: RTCProcessor with denoise_step
  - debug_handler.py: Tracker for debug information
  - debug_visualizer.py: Visualization utilities
- src/lerobot/policies/smolvla/modeling_smolvla.py: RTC integration
- examples/rtc/: Example scripts and evaluation tools

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
Co-Authored-By: Claude <noreply@anthropic.com>

* Fix rtc_config attribute access in SmolVLA

Use getattr() to safely check for rtc_config attribute existence
instead of direct attribute access. This fixes AttributeError when
loading policies without rtc_config in their config.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
Co-Authored-By: Claude <noreply@anthropic.com>

* fixup! Fix rtc_config attribute access in SmolVLA

* Add RTCConfig field to SmolVLAConfig

Add rtc_config as an optional field in SmolVLAConfig to properly
support Real-Time Chunking configuration. This replaces the previous
getattr() workarounds with direct attribute access, making the code
cleaner and more maintainable.

Changes:
- Import RTCConfig in configuration_smolvla.py
- Add rtc_config: RTCConfig | None = None field
- Revert getattr() calls to direct attribute access in modeling_smolvla.py

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
Co-Authored-By: Claude <noreply@anthropic.com>

* Refactor RTC enabled checks to use _rtc_enabled helper

Add _rtc_enabled() helper method in VLAFlowMatching class to simplify
and clean up RTC enabled checks throughout the code. This reduces
code duplication and improves readability.

Changes:
- Add _rtc_enabled() method in VLAFlowMatching
- Replace verbose rtc_config checks with _rtc_enabled() calls
- Maintain exact same functionality with cleaner code

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
Co-Authored-By: Claude <noreply@anthropic.com>

* Rename track_debug method to track

Simplify the method name from track_debug to just track for better
readability and consistency. The method already has clear documentation
about its debug tracking purpose.

Changes:
- Rename RTCProcessor.track_debug() to track()
- Update all call sites in modeling_smolvla.py and modeling_rtc.py

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
Co-Authored-By: Claude <noreply@anthropic.com>

* Use output_dir for saving all evaluation images

Update eval_dataset.py to save all comparison images to the
configured output_dir instead of the current directory. This provides
better organization and allows users to specify where outputs should be
saved.

Changes:
- Add os import at top level
- Create output_dir at start of run_evaluation()
- Save all comparison images to output_dir
- Remove duplicate os imports
- Update init_rtc_processor() docstring to be more concise

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>
Co-Authored-By: Claude <noreply@anthropic.com>

* fixup! Use output_dir for saving all evaluation images

* Fix logging buffering and enable tracking when RTC config provided

- Add force=True to logging.basicConfig to override existing configuration
- Enable line buffering for stdout/stderr for real-time log output
- Modify init_rtc_processor to create processor when rtc_config exists
  even if RTC is disabled, allowing tracking of denoising data

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>

* Refactor SmolVLA plotting to use tracker data instead of local variables

Remove local tracking variables (correction, x1_t, error) from the
denoising loop and instead retrieve plotting data from the RTC tracker
after each denoise step. This makes the code cleaner and uses the
tracker as the single source of truth for debug/visualization data.

Changes:
- Remove initialization of correction, x1_t, error before denoising loop
- After each Euler step, retrieve most recent debug step from tracker
- Extract correction, x1_t, err from debug step for plotting
- Update tracking condition to use is_debug_enabled() method

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>

* Move plotting logic from modeling_smolvla to eval_dataset script

Refactor to improve separation of concerns:

modeling_smolvla.py changes:
- Remove all plotting logic from sample_actions method
- Remove viz_xt_axs, viz_vt_axs, viz_x1t_axs parameters
- Remove matplotlib and RTCDebugVisualizer imports
- Remove viz_fig, viz_axs, denoise_step_counter instance variables
- Simplify denoising loop to only track data in rtc_processor

eval_dataset.py changes:
- Add _plot_denoising_steps_from_tracker helper method
- Retrieve debug steps from tracker after inference
- Plot x_t, v_t, x1_t, correction, and error from tracker data
- Enable debug tracking (cfg.rtc.debug = True) for visualization
- Remove viz axes parameters from predict_action_chunk calls

modeling_rtc.py changes:
- Remove v_t from track() call (handled by user change)

Benefits:
- Cleaner modeling code focused on inference
- Evaluation script owns all visualization logic
- Better separation of concerns
- Tracker is single source of truth for debug data

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>

* Refactor plotting loging

* fixup! Refactor plotting loging

* Improve visualization: separate correction plot and fix axis scaling

Changes:
- Create separate figure for correction data instead of overlaying on v_t
- Add _rescale_axes helper method to properly scale all axes
- Add 10% margin to y-axis for better visualization
- Fix v_t chart vertical compression issue

Benefits:
- Clearer v_t plot without correction overlay
- Better axis scaling with proper margins
- Separate correction figure for focused analysis
- Improved readability of all denoising visualizations

Output files:
- denoising_xt_comparison.png (x_t trajectories)
- denoising_vt_comparison.png (v_t velocity - now cleaner)
- denoising_correction_comparison.png (NEW - separate corrections)
- denoising_x1t_comparison.png (x1_t state with error)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Alexander Soare <alexander.soare159@gmail.com>

* fixup! Improve visualization: separate correction plot and fix axis scaling

* fixup! fixup! Improve visualization: separate correction plot and fix axis scaling

* fixup! fixup! fixup! Improve visualization: separate correction plot and fix axis scaling

* Fix traacking

* Right kwargs for the policy

* Add tests for tracker

* Fix tests

* Drop not required methods

* Add torch compilation for eval_dataset

* delete policies

* Add matplotliv to dev

* fixup! Add matplotliv to dev

* Experiemnt with late detach

* Debug

* Fix compilation

* Add RTC to PI0

* Pi0

* Pi0 eval dataset

* fixup! Pi0 eval dataset

* Turn off compilation for pi0/pi05

* fixup! Turn off compilation for pi0/pi05

* fixup! fixup! Turn off compilation for pi0/pi05

* fixup! fixup! fixup! Turn off compilation for pi0/pi05

* fixup! fixup! fixup! fixup! Turn off compilation for pi0/pi05

* fixup! fixup! fixup! fixup! fixup! Turn off compilation for pi0/pi05

* Add workable flow

* Small fixes

* Add more tests

* Add validatio at the end

* Update README

* Silent validation

* Fix tests

* Add tests for modeling_rtc

* Add tests for flow matching models with RTC

* fixup! Add tests for flow matching models with RTC

* fixup! fixup! Add tests for flow matching models with RTC

* Add one more test

* fixup! Add one more test

* Fix test to use _rtc_enabled() instead of is_rtc_enabled()

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fixup! Fix test to use _rtc_enabled() instead of is_rtc_enabled()

* fixup! fixup! Fix test to use _rtc_enabled() instead of is_rtc_enabled()

* Add RTC initialization tests without config for PI0.5 and SmolVLA

Add test_pi05_rtc_initialization_without_rtc_config and
test_smolvla_rtc_initialization_without_rtc_config to verify that
policies can initialize without RTC config and that _rtc_enabled()
returns False in this case.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* Fix PI0.5 init_rtc_processor to use getattr instead of direct model access

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* Fix SmolVLA init_rtc_processor to use getattr instead of direct model access

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* Fix PI0.5 RTC tests to use quantile stats (q01, q99) for normalization

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fixup! Fix PI0.5 RTC tests to use quantile stats (q01, q99) for normalization

* Fixup eval with real robot

* fixup! Fixup eval with real robot

* fixup! fixup! Fixup eval with real robot

* Extract simulator logic from eval_with real robot and add proper headers to files

* Update images

* Fix tests

* fixup! Fix tests

* add docs for rtc

* enhance doc and add images

* Fix instal instructions

---------
Co-authored-by: Ben Zhang <benzhangniu@gmail.com>
Co-authored-by: Alexander Soare <alexander.soare159@gmail.com>
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>

2025-11-19 11:19:48 +01:00

2 Commits