---
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 (%)