README.md

metadata

license: apache-2.0

MIP Checkpoints

Pre-trained checkpoints for the MIP (Minimum Iterative Policy) 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

# 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 (%)