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Pepijn
139 lines
6.1 KiB
Plaintext
139 lines
6.1 KiB
Plaintext
# UMI Data with pi0 Relative EE Actions
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This guide explains how to prepare a UMI-collected dataset for training a pi0 policy with relative end-effector (EE) actions, and how to run inference with the trained model.
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**What we will do:**
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1. How to add `observation.state` to an existing UMI LeRobot dataset.
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2. How to train pi0 with `use_relative_actions=True`.
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3. How to evaluate the trained policy on a real robot.
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## Background
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[UMI (Universal Manipulation Interface)](https://umi-gripper.github.io) collects manipulation data with hand-held grippers, recovering 6-DoF EE poses via SLAM. UMI datasets stored in LeRobot format already contain `action` (absolute EE pose) and wrist-camera images. To train pi0 with relative actions, we need two additions:
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1. **`observation.state`** — the current EE pose the policy conditions on.
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2. **Relative action statistics** — so the normalizer sees `(action − state)` distributions.
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### Why relative actions?
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With relative actions, each action in a chunk is an **offset from the current state** rather than an absolute target:
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```
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relative_action[i] = absolute_action[t + i] − state[t]
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```
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This is the representation advocated by UMI (Chi et al., 2024). Compared to absolute actions it removes the need for a consistent global coordinate frame, and compared to delta actions (each step relative to the previous) it avoids error accumulation across the chunk. See the [Action Representations](action_representations) guide for a full comparison.
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## State-Action Offset
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UMI SLAM produces a single trajectory of EE poses stored as `action`. We derive `observation.state` from the same trajectory with a configurable offset:
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```
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state[t] = action[t - offset]
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```
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| Offset | `state[t]` | Meaning |
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| ------ | ------------- | ---------------------------------------------------------------- |
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| 0 | `action[t]` | State and action are the same pose at time t |
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| 1 | `action[t-1]` | State is the previous action — where the gripper already arrived |
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An offset of 1 is the typical UMI convention: at decision time the "current state" is where the gripper _already is_ (the result of the previous command), and the action is where it should go next. At episode boundaries where `t < offset`, we clamp to `action[0]`.
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## Step 1: Add State and Recompute Stats
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The conversion script in `examples/umi_pi0_relative_ee/convert_umi_dataset.py` handles both steps. Edit the constants at the top of the file:
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```python
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HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
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STATE_ACTION_OFFSET = 1
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RELATIVE_EXCLUDE_JOINTS = ["gripper"]
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CHUNK_SIZE = 50
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```
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Then run:
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```bash
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python examples/umi_pi0_relative_ee/convert_umi_dataset.py
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```
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This:
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- Loads your existing UMI LeRobot dataset.
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- Adds `observation.state` derived from the `action` column with the configured offset.
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- Calls `recompute_stats(relative_action=True)` to compute chunk-level relative action statistics.
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The `RELATIVE_EXCLUDE_JOINTS` parameter specifies joints that stay absolute. Gripper commands are typically binary or continuous open/close and don't benefit from relative encoding.
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<Tip>
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If your dataset already has `observation.state`, the script skips the feature-adding step and only recomputes relative action statistics.
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</Tip>
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## Step 2: Train
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No custom training script is needed — standard `lerobot-train` handles everything:
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```bash
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lerobot-train \
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--dataset.repo_id=<hf_username>/<dataset_repo_id> \
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--policy.type=pi0 \
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--policy.pretrained_path=lerobot/pi0_base \
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--policy.use_relative_actions=true \
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--policy.relative_exclude_joints='["gripper"]'
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```
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Under the hood, the training pipeline:
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- Loads relative action stats from the dataset's `meta/stats.json`.
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- Configures `RelativeActionsProcessorStep` in the preprocessor (absolute → relative before normalization).
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- The model trains on normalized relative action values.
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See the [pi0 documentation](pi0) for all available training options.
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## Step 3: Evaluate
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The evaluation script in `examples/umi_pi0_relative_ee/evaluate.py` runs inference on a real robot (SO-100 with EE space):
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```bash
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python examples/umi_pi0_relative_ee/evaluate.py
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```
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Edit `HF_MODEL_ID`, `HF_DATASET_ID`, and robot configuration at the top of the file.
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The inference flow uses pi0's built-in processor pipeline — no custom wrappers needed:
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1. **Robot → FK** — Joint positions are converted to EE pose via `ForwardKinematicsJointsToEE`, producing `observation.state`.
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2. **Preprocessor** — `RelativeActionsProcessorStep` caches the raw `observation.state`, then `NormalizerProcessorStep` normalizes everything.
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3. **pi0 inference** — The model predicts a normalized relative action chunk.
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4. **Postprocessor** — `UnnormalizerProcessorStep` unnormalizes, then `AbsoluteActionsProcessorStep` adds the cached state back to get absolute EE targets.
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5. **IK → Robot** — `InverseKinematicsEEToJoints` converts absolute EE targets to joint commands.
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## How the Pieces Fit Together
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```
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Training:
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dataset (absolute EE) → RelativeActionsProcessorStep → NormalizerProcessorStep → pi0 model
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Inference:
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robot joints → FK → observation.state (absolute EE)
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↓
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RelativeActionsProcessorStep (caches state)
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↓
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NormalizerProcessorStep → pi0 model → relative action chunk
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↓
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UnnormalizerProcessorStep
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↓
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AbsoluteActionsProcessorStep (+ cached state → absolute EE)
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↓
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IK → joint targets → robot
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```
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## References
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- [UMI: Universal Manipulation Interface](https://umi-gripper.github.io) — Chi et al., 2024. Defines relative trajectory actions.
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- [Action Representations](action_representations) — LeRobot guide comparing absolute, relative, and delta actions.
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- [pi0 documentation](pi0) — Full pi0 configuration including `use_relative_actions`.
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- [`examples/so100_to_so100_EE/`](https://github.com/huggingface/lerobot/tree/main/examples/so100_to_so100_EE) — EE-space evaluation example this builds on.
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