| --- |
| 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 (%) |
| |
