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license: apache-2.0
---
# MIP Checkpoints
Pre-trained checkpoints for the [MIP (Minimum Iterative Policy)](https://github.com/simchowitzlabpublic/much-ado-fresh) framework.
## Repository Structure
```
robomimic/
{task}_{env_type}_{obs_type}/
delta_legacy/ # Original checkpoints (rot6d repr + delta controller)
abs/ # Absolute action space checkpoints
delta/ # Delta action space checkpoints (7D, no rot6d)
rel/ # Relative action space checkpoints
pusht/ # PushT environment checkpoints
kitchen/ # Kitchen environment checkpoints
```
## Robomimic Action Spaces
| Action Space | Config Suffix | `abs_action` | `action_type` | Dataset | `act_dim` (single/dual) |
|---|---|---|---|---|---|
| **delta_legacy** | `_delta_legacy` | `true` | `delta` | `low_dim.hdf5` | 10 / 20 |
| **absolute** | `_abs` | `true` | `absolute` | `low_dim_abs.hdf5` | 10 / 20 |
| **delta** | `_delta` | `false` | `delta` | `low_dim.hdf5` | 7 / 14 |
| **relative** | `_rel` | `true` | `relative` | `low_dim_abs.hdf5` | 10 / 20 |
> **Important:** The majority of released robomimic checkpoints (under `delta_legacy/`) were trained
> with the **delta_legacy** action space. You **must** use the corresponding `_delta_legacy` task
> config to evaluate them correctly. Using the default config (which uses absolute actions) will
> result in 0% success rate due to normalizer and controller mismatches.
## Quick Start: Evaluating a Checkpoint
```bash
# Download and evaluate a delta_legacy checkpoint
uv run examples/train_robomimic.py \
mode=eval \
task=lift_ph_state_delta_legacy \
network=chiunet \
optimization.loss_type=mip \
optimization.model_path="path/to/lift_ph_state_mip_chiunet_256_seed3_success100.pt"
```
### Available Task Configs
Each robomimic task has configs for all four action spaces:
- `lift_ph_state_delta_legacy`, `lift_ph_state_abs`, `lift_ph_state_delta`, `lift_ph_state_rel`
- `can_ph_state_delta_legacy`, `can_ph_state_abs`, `can_ph_state_delta`, `can_ph_state_rel`
- `square_ph_state_delta_legacy`, `square_ph_state_abs`, `square_ph_state_delta`, `square_ph_state_rel`
- `tool_hang_ph_state_delta_legacy`, `tool_hang_ph_state_abs`, `tool_hang_ph_state_delta`, `tool_hang_ph_state_rel`
- `transport_ph_state_delta_legacy`, `transport_ph_state_abs`, `transport_ph_state_delta`, `transport_ph_state_rel`
The `_mh` (multi-human) variants are also available (e.g., `lift_mh_state_delta_legacy`).
## Checkpoint Naming Convention
```
{loss_type}_{network}_{dim}_seed{N}_success{N}.pt
```
- **loss_type**: `mip`, `flow`, `regression`, `psd`, `lsd`, `straight_flow`
- **network**: `chiunet`, `chitransformer`, `mlp`, `sudeepdit`, `rnn`
- **dim**: embedding dimension (e.g., `256`, `384`, `512`)
- **seed**: random seed
- **success**: best evaluation success rate (%)
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