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Pepijn
147 lines
7.5 KiB
Plaintext
147 lines
7.5 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. Recompute dataset statistics for relative actions and state.
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2. Train pi0 with `derive_state_from_action=true` (full UMI pipeline).
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3. 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 **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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### Full UMI mode: `derive_state_from_action`
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When `derive_state_from_action=true`, pi0 automatically derives `observation.state` from the action column on the fly — no separate state column or dataset conversion step needed. Under the hood:
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- `action_delta_indices` extends to `[-1, 0, 1, ..., 49]` (one extra leading timestep).
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- `DeriveStateFromActionStep` extracts `[action[t-1], action[t]]` as a 2-step state and strips the extra timestep from the action chunk.
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- `RelativeActionsProcessorStep` converts actions to offsets from `state[t]`.
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- `RelativeStateProcessorStep` converts the 2-step state to relative proprioception (velocity + zeros) and flattens.
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This single flag implies `use_relative_state=true` and `state_obs_steps=2`.
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During **inference**, state comes from the robot (via FK), so `DeriveStateFromActionStep` is a no-op. `RelativeStateProcessorStep` buffers the previous state and applies the same conversion automatically.
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## Step 1: Recompute Stats
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Use the built-in CLI to recompute dataset statistics for relative actions and derive-state-from-action:
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```bash
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lerobot-edit-dataset \
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--repo_id <your_dataset> \
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--operation.type recompute_stats \
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--operation.relative_action true \
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--operation.derive_state_from_action true \
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--operation.chunk_size 50 \
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--operation.relative_exclude_joints "['gripper']" \
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--push_to_hub true
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```
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The `derive_state_from_action` flag tells `recompute_stats` to read from the action column (instead of `observation.state`) when computing relative state stats. It automatically enables `relative_state=true` and `state_obs_steps=2`.
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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. Leave it as `"[]"` to convert all dimensions to relative.
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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.derive_state_from_action=true \
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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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`derive_state_from_action=true` auto-enables `use_relative_state=true` and `state_obs_steps=2`.
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Under the hood, the training pipeline:
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- Loads relative action stats and relative state stats from the dataset's `meta/stats.json`.
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- Extends `action_delta_indices` to `[-1, 0, 1, ..., 49]` to load one extra leading timestep.
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- `DeriveStateFromActionStep` extracts the 2-step state from the action chunk and strips the extra timestep.
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- `RelativeActionsProcessorStep` converts actions to offsets from `state[t]`.
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- `RelativeStateProcessorStep` converts the 2-step state to relative offsets from the current timestep, then flattens.
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- `NormalizerProcessorStep` normalizes everything.
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- The model trains on normalized relative 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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### Latency compensation
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For real robot deployment, you may want to skip the first few steps of each predicted action chunk to compensate for system latency. Set `LATENCY_SKIP_STEPS` in the evaluate script:
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```python
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LATENCY_SKIP_STEPS = 0 # ceil(total_latency_ms / (1000 / FPS))
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```
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For example, at 10Hz with ~200ms total latency, set `LATENCY_SKIP_STEPS = 2`.
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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** — `DeriveStateFromActionStep` is a no-op (state comes from robot). `RelativeStateProcessorStep` buffers previous state, stacks, and converts to relative. `RelativeActionsProcessorStep` caches state. `NormalizerProcessorStep` normalizes.
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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 (full UMI mode: derive_state_from_action=true):
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DataLoader (action: B,51,D)
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→ DeriveStateFromActionStep (state = action[:,:2,:], action = action[:,1:,:])
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→ RelativeActionsProcessorStep (action -= state[:,-1,:])
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→ RelativeStateProcessorStep (state offsets from current, flatten → B,2*D)
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→ 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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DeriveStateFromActionStep (no-op)
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↓
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RelativeActionsProcessorStep (caches state)
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↓
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RelativeStateProcessorStep (buffers prev, stacks, subtracts, flattens)
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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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