mu-vla/mu-vla-openvla-oft-mikasa-robo-5-tasks-m64-k8-tbptt

mu-VLA (OpenVLA-OFT + recurrent memory), MIKASA-Robo, 64 memory tokens, TBPTT K=8

mu-VLA extends OpenVLA-OFT with a recurrent memory module so that the policy can act in partially observable Markov decision processes (POMDPs), where the information needed for the current action appeared earlier in the episode and is no longer visible. Vanilla OpenVLA-OFT is a per-step policy and cannot solve such tasks.

Memory is implemented as a set of learnable memory tokens that are injected into the multimodal prefix of the transformer sequence and carried across environment steps. Gradients flow through a truncated backpropagation-through-time (TBPTT) window; the memory state is reset at episode boundaries.

This checkpoint corresponds to experiment 4 in the mu-VLA experiment matrix: multitask training on 5 MIKASA-Robo-VLA environments, 64 memory tokens, TBPTT window K=8.

Sibling checkpoints:

• mu-vla-openvla-oft-mikasa-robo-5-tasks-m64-k2-tbptt, same setup with K=2
• mu-vla-openvla-oft-libero-4-tasks-m64-k8-tbptt, same memory setup on LIBERO

Memory configuration

Setting	Value
num_mem_tokens	64
memory_update	tbptt
tbptt_length	8
attention_mask_mode	custom
ema_alpha	0.1 (unused when memory_update=tbptt)

The same values are shipped in memory_meta.json so that the evaluation scripts in the code repository pick them up automatically.

Training setup

Base model: openvla/openvla-7b, fine-tuned with the OpenVLA-OFT recipe (L1 regression action head, proprioception projector, two input camera views, no FiLM, no diffusion).

Setting	Value
Checkpoint step	150000
Dataset	MIKASARoboVLAEpisodicDataset, mikasa_five
Training environments	ShellGamePush-VLA-v0, InterceptMedium-VLA-v0, RememberColor5-VLA-v0, TakeItBack-VLA-v0, RememberShapeAndColor3x3-VLA-v0
GPUs	8 x NVIDIA A100 80GB
Batch size	4 per GPU
LoRA rank	32
Learning rate	5e-4, cosine schedule, 2000 warmup steps, minimum ratio 0.1
Image augmentation	on
Gradient checkpointing	on

The episodic dataloader feeds batch_size independent streams, each replaying whole episodes in order, and marks every step with is_first and is_last so that the memory state can be reset on episode boundaries. Action normalisation statistics (q01/q99) are computed jointly over all five environments and stored under the mikasa_combined key in dataset_statistics.json.

Evaluation

Protocol: each environment is evaluated independently, 100 episodes, starting seed 4242424242, metric success_once. Uncertainty is the standard error over 100 Bernoulli trials.

Inference uses receding horizon: instead of executing the whole open-loop chunk of 8 predicted actions, the model is queried every step and only the first action is executed. This matters for POMDP tasks where the visual cue is visible only briefly. The cost is 8 times more forward passes.

Only the first five rows below are training environments. Everything else is zero-shot transfer to environments the model never saw during fine-tuning.

Environment	mu-VLA K=8	OpenVLA-OFT baseline
ShellGamePush-VLA-v0 (train)	0.93 ± 0.03	0.33
InterceptMedium-VLA-v0 (train)	0.44 ± 0.05	0.53
RememberColor5-VLA-v0 (train)	0.40 ± 0.05	0.23
TakeItBack-VLA-v0 (train)	0.99 ± 0.01	0.98
RememberShapeAndColor3x3-VLA-v0 (train)	0.09 ± 0.03	0.11
ShellGameTouch-VLA-v0	0.00 ± 0.00	0.00
ShellGamePick-VLA-v0	0.01 ± 0.01	0.01
InterceptSlow-VLA-v0	0.06 ± 0.02	0.06
InterceptFast-VLA-v0	0.19 ± 0.04	0.24
InterceptGrabSlow-VLA-v0	0.00 ± 0.00	0.00
InterceptGrabMedium-VLA-v0	0.00 ± 0.00	0.00
InterceptGrabFast-VLA-v0	0.00 ± 0.00	0.00
RotateLenientPos-VLA-v0	0.00 ± 0.00	0.08
RotateLenientPosNeg-VLA-v0	0.06 ± 0.02	0.07
RotateStrictPos-VLA-v0	0.04 ± 0.02	0.03
RotateStrictPosNeg-VLA-v0	0.06 ± 0.02	0.05
RememberColor3-VLA-v0	0.38 ± 0.05	0.25
RememberColor9-VLA-v0	0.09 ± 0.03	0.07
RememberShape3-VLA-v0	0.35 ± 0.05	0.17
RememberShape5-VLA-v0	0.20 ± 0.04	0.15
RememberShape9-VLA-v0	0.09 ± 0.03	0.11
RememberShapeAndColor3x2-VLA-v0	0.11 ± 0.03	0.12
RememberShapeAndColor5x3-VLA-v0	0.15 ± 0.04	0.08

Baseline column: OpenVLA-OFT without memory, trained on the same five environments with the same episodic dataloader (experiment 2 in the matrix). It is not a perfectly matched control, since it used the constant-then-decay learning rate schedule rather than cosine.

Note on the TBPTT window: the K=2 sibling checkpoint is substantially stronger on the memory-heavy environments (for example 0.94 versus 0.40 on RememberColor5-VLA-v0). If you are looking for the best MIKASA-Robo memory policy of the two, start there. This checkpoint is published as the K=8 point of the ablation.

Files

Loading this checkpoint requires more than the base transformer weights. The extra heads are stored as separate PyTorch state dicts, matching the layout the training and evaluation scripts expect.

File	Purpose
model-0000*-of-00004.safetensors, model.safetensors.index.json	merged vision-language backbone
lora_adapter/	LoRA adapter as saved by PEFT, before merging
action_head--150000_checkpoint.pt	L1 regression action head
proprio_projector--150000_checkpoint.pt	proprioception projector
memory_module--150000_checkpoint.pt	memory module, including initial_memory
memory_meta.json	memory hyperparameters for evaluation auto-detection
dataset_statistics.json	action normalisation statistics, key mikasa_combined
optimizer--150000_checkpoint.pt, scheduler--150000_checkpoint.pt	optimizer and scheduler state, for resuming training
configuration_prismatic.py, modeling_prismatic.py, processing_prismatic.py	custom model code, memory token injection and the custom attention mask live here

Usage

The checkpoint is not loadable with upstream transformers. It needs the mu-VLA code, which depends on a forked transformers that supports the custom attention mask, and on MIKASA-Robo for the environments.

huggingface-cli download mu-vla/mu-vla-openvla-oft-mikasa-robo-5-tasks-m64-k8-tbptt \
  --local-dir ./mu-vla-mikasa-m64-k8

Then point the evaluation script at the downloaded directory; memory hyperparameters are read from memory_meta.json. See the code repository, CognitiveAISystems/mu-vla, for setup and the exact command line.

Attribution

mu-VLA is a fork of OpenVLA-OFT by Moo Jin Kim, Chelsea Finn and Percy Liang (arXiv:2502.19645), which in turn builds on OpenVLA. The recurrent memory module, the episodic dataloaders and the MIKASA-Robo integration are the contribution of this work. Released under the MIT license, following upstream.