This directory contains examples and evaluation scripts for Real-Time Chunking (RTC), a technique for improving action chunking policies in real-time robot control.

Overview

Real-Time Chunking addresses the challenge of maintaining consistency and reactivity when using action chunking policies with non-negligible inference latency. It uses a guidance technique during diffusion sampling to blend new action predictions with previously planned actions.

Key Benefits:

Maintains consistency between consecutive action chunks
Reduces jitter and improves smoothness
Adapts to inference delays dynamically

Reference: Physical Intelligence - Real-Time Chunking

Scripts

1. `eval_dataset.py`

Offline evaluation on dataset samples with detailed visualization and validation.

Features:

Compare RTC vs non-RTC predictions on two random dataset samples
Validate RTC behavior (delay region, blend region, post-horizon region)
Generate debug visualizations:
- Denoising step comparisons (x_t, v_t, x1_t, corrections)
- Final action predictions comparison
Support for torch.compile() optimization
Memory-efficient sequential policy loading for large models

Usage:

# Basic usage with SmolVLA policy
uv run python examples/rtc/eval_dataset.py \
    --policy.path=helper2424/smolvla_check_rtc_last3 \
    --dataset.repo_id=helper2424/check_rtc \
    --rtc.execution_horizon=8 \
    --device=mps \
    --rtc.max_guidance_weight=10.0 \
    --seed=10

# With Pi0.5 policy on CUDA
uv run python examples/rtc/eval_dataset.py \
    --policy.path=lerobot/pi05_libero_finetuned \
    --dataset.repo_id=HuggingFaceVLA/libero \
    --rtc.execution_horizon=8 \
    --device=cuda

# With Pi0 policy
uv run python examples/rtc/eval_dataset.py \
    --policy.path=lerobot/pi0_libero_finetuned \
    --dataset.repo_id=HuggingFaceVLA/libero \
    --rtc.execution_horizon=8 \
    --device=cuda

# With torch.compile for faster inference
uv run python examples/rtc/eval_dataset.py \
    --policy.path=helper2424/smolvla_check_rtc_last3 \
    --dataset.repo_id=helper2424/check_rtc \
    --rtc.execution_horizon=8 \
    --device=cuda \
    --use_torch_compile=true \
    --torch_compile_mode=max-autotune

# Enable CUDA graphs (advanced - may cause tensor aliasing errors)
uv run python examples/rtc/eval_dataset.py \
    --policy.path=helper2424/smolvla_check_rtc_last3 \
    --dataset.repo_id=helper2424/check_rtc \
    --use_torch_compile=true \
    --torch_compile_backend=inductor \
    --torch_compile_mode=max-autotune \
    --torch_compile_disable_cudagraphs=false

Key Parameters:

--policy.path: Path to pretrained policy
--dataset.repo_id: Dataset to evaluate on
--rtc.execution_horizon: Number of steps to maintain consistency (default: 20)
--rtc.max_guidance_weight: Maximum guidance weight (default: 10.0)
--rtc.prefix_attention_schedule: Schedule type (ZEROS, ONES, LINEAR, EXP)
--inference_delay: Inference delay for RTC (default: 4)
--seed: Random seed for reproducibility (default: 42)
--output_dir: Directory to save visualizations (default: rtc_debug_output)
--device: Device to use (cuda, cpu, mps, auto)
--use_torch_compile: Enable torch.compile() for faster inference

Output:

The script generates several visualization files in rtc_debug_output/:

denoising_xt_comparison.png - Noisy state evolution during denoising
denoising_vt_comparison.png - Velocity predictions during denoising
denoising_x1t_comparison.png - Predicted final states during denoising
denoising_correction_comparison.png - RTC guidance corrections applied
final_actions_comparison.png - Final action predictions (prev_chunk, no_rtc, rtc)

The script also validates RTC behavior and reports:

✅ Delay region [0:inference_delay]: RTC = prev_chunk
✅ Blend region [inference_delay:execution_horizon]: prev_chunk ≤ RTC ≤ no_rtc
✅ Post-horizon [execution_horizon:]: RTC = no_rtc

2. `eval_with_real_robot.py`

Real-time evaluation on physical robots or simulation environments.

Features:

Run policy with RTC on real robot or simulation
Multi-threaded action execution and inference
Action queue management with proper timing
Latency tracking and adaptive inference delay
Support for both robots and gym environments
Support for torch.compile() optimization

Usage:

# With real robot
uv run python examples/rtc/eval_with_real_robot.py \
    --policy.path=lerobot/smolvla_base \
    --robot.type=so100 \
    --task="pick up the cup" \
    --duration=30.0

# With simulation environment
uv run python examples/rtc/eval_with_real_robot.py \
    --policy.path=lerobot/smolvla_base \
    --env.type=pusht \
    --duration=60.0

# With policy compilation (CUDA only, not MPS)
uv run python examples/rtc/eval_with_real_robot.py \
    --policy.path=lerobot/smolvla_base \
    --robot.type=so100 \
    --use_torch_compile=true \
    --torch_compile_mode=max-autotune

Key Parameters:

--policy.path: Path to pretrained policy
--robot.type or --env.type: Robot or environment to use
--task: Task description (for VLA models)
--rtc.execution_horizon: Number of steps to maintain consistency (default: 10)
--rtc.max_guidance_weight: Maximum guidance weight (default: 1.0)
--rtc.prefix_attention_schedule: Schedule type (ZEROS, ONES, LINEAR, EXP)
--duration: How long to run (seconds, default: 30.0)
--fps: Action execution frequency (Hz, default: 10.0)
--action_queue_size_to_get_new_actions: Queue size threshold to request new actions (default: 30)
--device: Device to use (cuda, cpu, mps, auto)
--use_torch_compile: Enable torch.compile() for faster inference

Understanding RTC Parameters

`execution_horizon`

Number of timesteps from previous chunk to maintain consistency with. Higher values mean more consistency but potentially less reactivity.

Typical values: 8-12 steps for dataset evaluation, 10 steps for real-time execution

`max_guidance_weight`

Upper bound on guidance strength. Higher values give stronger consistency but may over-constrain new predictions.

Typical values:

Dataset evaluation: 10.0-100.0 (can be higher for analysis)
Real-time execution: 1.0-10.0 (more conservative)

`prefix_attention_schedule`

How to weight consistency across the overlap region:

ZEROS: Binary (full weight up to inference_delay, then zero)
ONES: Full weight across entire execution_horizon
LINEAR: Linear decay from inference_delay to execution_horizon
EXP: Exponential decay (recommended)

Recommended: EXP

`inference_delay`

Number of timesteps from the prefix to use for guidance. Typically calculated dynamically based on inference latency in real-time execution, but fixed for dataset evaluation.

Typical values: 3-5 steps for dataset evaluation

`action_queue_size_to_get_new_actions` (real-time only)

Threshold for requesting new action chunks. Should be higher than inference_delay + execution_horizon to ensure smooth operation.

Typical values: 20-30 steps

Validation Rules (Dataset Evaluation)

The dataset evaluation script validates that RTC behavior matches expectations:

Delay Region [0:inference_delay]: RTC actions should equal previous chunk
- Ensures consistency during the inference delay period
Blend Region [inference_delay:execution_horizon]: RTC should be between prev_chunk and no_rtc
- Smooth transition from previous plan to new predictions
Post-Horizon [execution_horizon:]: RTC should equal no_rtc
- Full adoption of new predictions after execution horizon

Tips

Start with dataset evaluation (eval_dataset.py) to understand RTC behavior and tune parameters before running on robot
Use visualizations to debug unexpected behavior - check denoising steps and final actions
Tune execution_horizon based on your inference latency and action frequency
Monitor validation output - failures indicate potential implementation issues or misconfigured parameters
Compare different schedules - EXP usually works best but LINEAR can be more interpretable

Troubleshooting

Validation fails in delay region

Check that prev_chunk_left_over is properly passed to the policy
Verify RTC guidance is being applied during denoising
Look at denoising visualizations to see where guidance diverges

Validation fails in post-horizon region

RTC and no_rtc use different noise - verify same noise is being used for comparison
Check that weights are correctly zeroed out after execution horizon
Review prefix_attention_schedule visualization

Poor performance on real robot

Increase action_queue_size_to_get_new_actions if you see warnings
Reduce max_guidance_weight if robot is too conservative
Try different prefix_attention_schedule values
Enable torch.compile() for faster inference (CUDA only)

Memory issues with large models

The dataset evaluation script loads policies sequentially to minimize memory
For real-time execution, only one policy is loaded
Use smaller batch sizes if needed

README.md

Real-Time Chunking (RTC) Examples

Overview

Scripts

1. eval_dataset.py

2. eval_with_real_robot.py