Add Robometer code + Robometer-4B weights

.gitattributes

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models/Robometer-4B/.gitattributes

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models/Robometer-4B/README.md

@@ -0,0 +1,66 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-VL-4B-Instruct
+tags:
+  - reward model
+  - robot learning
+  - foundation models
+library_name: transformers
+---
+
+# Robometer 4B
+
+**Paper:** [arXiv (Coming Soon)](https://arxiv.org/)
+
+**Robometer** is a general-purpose vision-language reward model for robotics. It is trained on [RBM-1M](https://huggingface.co/datasets/) with **Qwen3-VL-4B** to predict **per-frame progress**, **per-frame success**, and **trajectory preferences** from rollout videos. The model combines (1) frame-level progress supervision on expert data and (2) trajectory-comparison preference supervision, so it can learn from both successful and failed rollouts and generalize across diverse robot embodiments and tasks.
+
+Given a **task instruction** and a **rollout video** (or frame sequence), the model predicts:
+
+- **Per-frame progress** — continuous progress values over time (e.g. 0–1 or binned).
+- **Per-frame success** — success probability (or binary) at each timestep.
+- **Preference / ranking** — which of two trajectories is better for the task.
+
+### Usage
+
+For full setup, example scripts, and configs, see the **GitHub repo**: [github.com/robometer/robometer](https://github.com/robometer/robometer).
+
+**Option 1 — Run the model locally** (loads this checkpoint from Hugging Face):
+
+```bash
+uv run python scripts/example_inference_local.py \
+  --model-path robometer/Robometer-4B \
+  --video /path/to/video.mp4 \
+  --task "your task description"
+```
+
+**Option 2 — Use the evaluation server** (start server, then run client):
+
+```bash
+# Start server
+uv run python robometer/evals/eval_server.py \
+  --config-path=robometer/configs \
+  --config-name=eval_config_server \
+  model_path=robometer/Robometer-4B \
+  server_url=0.0.0.0 \
+  server_port=8000
+
+# Client (no robometer dependency)
+uv run python scripts/example_inference.py \
+  --eval-server-url http://localhost:8000 \
+  --video /path/to/video.mp4 \
+  --task "your task description"
+```
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{liang2026robometer,
+  title={Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons},
+  author={Anthony Liang* and Yigit Korkmaz* and Jiahui Zhang and Minyoung Hwang and Abrar Anwar and Sidhant Kaushik and Aditya Shah and Alex S. Huang and Luke Zettlemoyer and Dieter Fox and Yu Xiang and Anqi Li and Andreea Bobu and Abhishek Gupta and Stephen Tu† and Erdem B{\i}y{\i}k† and Jesse Zhang†},
+  year={2026},
+  url={https://github.com/robometer/robometer},
+  note={arXiv coming soon}
+}
+```

models/Robometer-4B/config.json

@@ -0,0 +1,68 @@
+{
+  "architectures": [
+    "RFM"
+  ],
+  "dtype": "bfloat16",
+  "image_token_id": 151655,
+  "model_type": "qwen3_vl",
+  "pad_token_id": 151654,
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 9728,
+    "max_position_embeddings": 262144,
+    "model_type": "qwen3_vl_text",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": {
+      "mrope_interleaved": true,
+      "mrope_section": [
+        24,
+        20,
+        20
+      ],
+      "rope_type": "default"
+    },
+    "rope_theta": 5000000,
+    "tie_word_embeddings": true,
+    "use_cache": true,
+    "vocab_size": 151674
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.2",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.10.11",
+  "video_token_id": 151656,
+  "vision_config": {
+    "deepstack_visual_indexes": [
+      5,
+      11,
+      17
+    ],
+    "depth": 24,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1024,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "model_type": "qwen3_vl",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 2560,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652
+}

models/Robometer-4B/config.yaml

@@ -0,0 +1,187 @@
+custom_eval:
+  comparisons_per_task: 5
+  confusion_matrix:
+  - mw
+  custom_eval_random_seed: 42
+  eval_types:
+  - reward_alignment
+  - policy_ranking
+  num_examples_per_quality_pr: 5
+  num_partial_successes: 5
+  pad_frames: true
+  policy_ranking:
+  - rbm-1m-ood
+  policy_ranking_max_tasks: 100
+  quality_preference:
+  - mw
+  reward_alignment:
+  - rbm-1m-id
+  - rbm-1m-ood
+  reward_alignment_max_trajectories: 10
+  subsample_n_frames: null
+  use_frame_steps: true
+data:
+  data_source_weights:
+    metaworld_train: 1.0
+    molmoact_dataset_household: 1.0
+    molmoact_dataset_tabletop: 1.0
+    oxe_droid: 1.0
+    roboarena: 1.0
+  dataloader_num_workers: 8
+  dataloader_persistent_workers: true
+  dataloader_pin_memory: true
+  dataset_preference_ratio: 0.7
+  dataset_success_cutoff_file: robometer/data/dataset_success_cutoff.txt
+  dataset_type: strategy_first
+  eval_datasets:
+  - mw
+  eval_subset_size: null
+  load_embeddings: false
+  max_frames: 8
+  max_frames_after_preprocessing: 64
+  max_success: 1.0
+  max_trajectories: -1
+  min_frames_per_trajectory: 5
+  min_success: 0.5
+  partial_success_threshold: 0.2
+  predict_last_frame_partial_progress: false
+  preference_strategy_ratio:
+  - 1.0
+  - 1.0
+  - 1.0
+  - 1.0
+  progress_discrete_bins: 10
+  progress_loss_type: discrete
+  progress_pred_type: absolute_wrt_total_frames
+  progress_strategy_ratio:
+  - 1.0
+  - 1.0
+  - 1.0
+  - 1.0
+  resized_height: null
+  resized_width: null
+  sample_type_ratio:
+  - 1.0
+  - 0.0
+  - 0.0
+  seed: 42
+  shuffle: true
+  shuffle_progress_frames: false
+  train_datasets:
+  - rbm-1m-id
+  traj_same_source_prob: 0.5
+  use_multi_image: true
+  use_per_frame_progress_token: true
+debug: false
+logging:
+  log_level: debug
+  log_to:
+  - wandb
+  save_best:
+    greater_is_better:
+    - true
+    - true
+    - true
+    - true
+    - true
+    hub_private: false
+    hub_save_every: 1000
+    hub_token: null
+    keep_top_k: 5
+    metric_names:
+    - eval_p_rank/kendall_last_utd_so101_clean_top
+    - eval_p_rank/kendall_last_usc_xarm
+    - eval_p_rank/kendall_last_usc_franka
+    - eval_p_rank/kendall_last_rfm_new_mit_franka_nowrist
+    - eval_p_rank/kendall_last_usc_trossen
+    save_every: 250
+    upload_to_hub: false
+  save_model: true
+  save_processor: true
+  wandb_entity: clvr
+  wandb_mode: null
+  wandb_notes: all run with prog_token per frame, qwen 4b, discrete progress, 10 bins
+  wandb_project: robometer
+loss:
+  predict_last_frame_progress: false
+  progress_discrete_bins: 10
+  progress_loss_type: discrete
+  success_positive_weight: 1.0
+mode: train
+model:
+  average_temporal_patches: true
+  base_model_id: Qwen/Qwen3-VL-4B-Instruct
+  frame_pooling: mean
+  frame_pooling_attn_temperature: 1.0
+  model_type: default
+  peft_vision_encoder: false
+  progress_discrete_bins: 10
+  progress_loss_type: discrete
+  quantization: false
+  rewind: null
+  torch_dtype: bfloat16
+  train_language_model: true
+  train_preference_head: true
+  train_progress_head: true
+  train_success_head: true
+  train_vision_encoder: false
+  trust_remote_code: true
+  use_multi_image: true
+  use_peft: false
+  use_per_frame_progress_token: true
+  use_unsloth: true
+peft:
+  bias: none
+  lora_alpha: 64
+  lora_dropout: 0.05
+  peft_vision_encoder: false
+  r: 32
+  target_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - gate_proj
+  - up_proj
+  - down_proj
+trainer_cls: rbm_heads
+training:
+  beta: 0.1
+  bf16: true
+  custom_eval_steps: 250
+  dataloader_num_workers: 8
+  dataloader_persistent_workers: true
+  dataloader_pin_memory: true
+  ddp_bucket_cap_mb: 25
+  ddp_find_unused_parameters: false
+  do_eval: true
+  eval_steps: 250
+  evaluation_strategy: steps
+  exp_name: ant_rfm_qwen4b_4gpu_bs16_pref_prog_succ_8_frames_all_discrete_10_bins_part2
+  fp16: false
+  gradient_accumulation_steps: 1
+  gradient_checkpointing: true
+  learning_rate: 2.0e-05
+  logging_steps: 1
+  lr_scheduler_type: cosine
+  max_grad_norm: 10.0
+  max_seq_length: 1024
+  max_steps: 15000
+  num_gpus: 2
+  num_train_epochs: -1
+  output_dir: ./logs
+  overwrite_output_dir: true
+  per_device_eval_batch_size: 16
+  per_device_train_batch_size: 16
+  predict_pref_progress: true
+  prediction_loss_only: true
+  remove_unused_columns: false
+  resume_from_checkpoint: /gpfs/home/jessezha/scrubbed_storage/reward_fm/logs/ant_rfm_qwen4b_4gpu_bs16_pref_prog_succ_8_frames_all_discrete_10_bins/ckpt-avg-5metrics=0.6973_step=3000
+  run_default_eval: false
+  save_steps: 200
+  save_strategy: 'no'
+  vision_encoder_lr: 5.0e-06
+  vision_encoder_num_layers: 3
+  warmup_ratio: 0.1
+  warmup_steps: 0
+  weight_decay: 0.01

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models/Robometer-4B/metrics.json

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models/Robometer-4B/training_args.bin

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robometer/FINETUNE_ROBOMETER.md

@@ -0,0 +1,197 @@
+# Fine-tuning Robometer on Your Own Data
+
+Preprocess a dataset, LoRA fine-tune from Robometer-4B, upload to the Hub, run inference. Example: [MINT-SJTU/RoboFAC-dataset](https://huggingface.co/datasets/MINT-SJTU/RoboFAC-dataset).
+
+---
+
+## 1. Preprocessing (RoboFAC)
+
+Training needs a **preprocessed** cache from a HuggingFace dataset in RBM format. RoboFAC is folder-based; convert first, then preprocess.
+
+1. **Download** (into `ROBOMETER_DATASET_PATH`):
+   ```bash
+   export ROBOMETER_DATASET_PATH=/path/to/your/robometer_dataset
+   huggingface-cli download MINT-SJTU/RoboFAC-dataset --local-dir $ROBOMETER_DATASET_PATH/RoboFAC-dataset
+   ```
+
+2. **Convert and push to Hub** (required for recommended flow):
+   ```bash
+   export HF_TOKEN=your_token_here
+
+   uv run python -m dataset_upload.generate_hf_dataset \
+     --config_path dataset_upload/configs/data_gen_configs/robofac.yaml \
+     --dataset.dataset_path=$ROBOMETER_DATASET_PATH/RoboFAC-dataset \
+     --hub.push_to_hub=true \
+     --hub.hub_repo_id=robofac_rbm
+   ```
+
+3. **If you pushed:** download converted repo and set preprocess config:
+   ```bash
+   huggingface-cli download aliangdw/robofac_rbm --local-dir $ROBOMETER_DATASET_PATH/robofac_rbm
+   ```
+   In `preprocess_finetune.yaml`: `train_datasets: ["aliangdw/robofac_rbm"]`, `train_subsets: [["robofac"]]`.
+
+4. **Preprocess:**
+   ```bash
+   export ROBOMETER_PROCESSED_DATASETS_PATH=/path/to/save/processed_datasets
+
+   uv run python -m robometer.data.scripts.preprocess_datasets \
+     --config robometer/configs/preprocess_finetune.yaml \
+     --cache_dir=$ROBOMETER_PROCESSED_DATASETS_PATH
+   ```
+   Use the same path for training.
+
+**Other datasets:** RBM-style Hub datasets: set `train_datasets`/`train_subsets` and `ROBOMETER_DATASET_PATH` in the preprocess config, then run step 4. Raw data: add a loader (see [CustomDataset.md](dataset_upload/dataset_guides/CustomDataset.md)).
+
+---
+
+## 2. LoRA fine-tuning
+
+Use PEFT (LoRA) and `load_from_checkpoint` from a Qwen3-4B–based RBM checkpoint.
+
+```bash
+export ROBOMETER_PROCESSED_DATASETS_PATH=/path/to/your/processed_datasets
+
+uv run python train.py \
+  model.base_model_id=Qwen/Qwen3-VL-4B-Instruct \
+  model.use_peft=true \
+  model.train_progress_head=true \
+  model.train_preference_head=true \
+  data.train_datasets=[aliangdw_robofac_rbm_robofac] \
+  data.eval_datasets=[mw] \
+  training.load_from_checkpoint=aliangdw/Robometer-4B \
+  training.per_device_train_batch_size=8 \
+  training.learning_rate=2e-5 \
+  training.warmup_ratio=0.1 \
+  training.weight_decay=0.01 \
+  training.max_steps=1000 \
+  training.output_dir=./logs \
+  training.exp_name=robometer4b_lora_robofac_2 \
+  logging.log_to=[wandb] \
+  custom_eval.eval_types=[reward_alignment,policy_ranking] \
+  custom_eval.reward_alignment=[aliangdw_robofac_rbm_robofac] \
+  custom_eval.policy_ranking=[aliangdw_robofac_rbm_robofac] \
+  logging.save_best.metric_names=[eval_rew_align/pearson_robofac,eval_p_rank/kendall_last_robofac] \
+  logging.save_best.greater_is_better=[true,true] \
+  training.overwrite_output_dir=True \
+  training.eval_steps=50 \
+  training.custom_eval_steps=50
+```
+
+The short name `robofac` is defined in `name_mapping.py` for `aliangdw_robofac_rbm_robofac`.
+
+**Tunable LoRA / training:** Override `training.learning_rate`, `training.warmup_ratio`, `training.weight_decay`, `training.gradient_accumulation_steps`, or `training.max_steps` as needed. Defaults above match `robometer/configs/config.yaml`. Multi-GPU: `uv run accelerate launch --config_file robometer/configs/distributed/fsdp.yaml train.py ...` (same overrides).
+
+### Full fine-tuning (no PEFT)
+
+Load the same checkpoint but train the full model (no LoRA). Uses more memory; lower `per_device_train_batch_size` or gradient accumulation if needed.
+
+```bash
+export ROBOMETER_PROCESSED_DATASETS_PATH=/path/to/your/processed_datasets
+
+uv run python train.py \
+  model.base_model_id=Qwen/Qwen3-VL-4B-Instruct \
+  model.use_peft=false \
+  model.train_progress_head=true \
+  model.train_preference_head=true \
+  data.train_datasets=[aliangdw_robofac_rbm_robofac] \
+  data.eval_datasets=[aliangdw_robofac_rbm_robofac] \
+  training.load_from_checkpoint=aliangdw/Robometer-4B \
+  training.per_device_train_batch_size=8 \
+  training.learning_rate=2e-5 \
+  training.warmup_ratio=0.1 \
+  training.weight_decay=0.01 \
+  training.gradient_accumulation_steps=1 \
+  training.max_steps=500 \
+  training.output_dir=./logs \
+  training.exp_name=robometer4b_full_robofac \
+  logging.log_to=[wandb] \
+  custom_eval.reward_alignment=[aliangdw_robofac_rbm_robofac] \
+  custom_eval.policy_ranking=[aliangdw_robofac_rbm_robofac] \
+  logging.save_best.metric_names=[eval_rew_align/pearson_robofac,eval_p_rank/kendall_last_robofac] \
+  logging.save_best.greater_is_better=[true,true] \
+  training.eval_steps=50 \
+  training.custom_eval_steps=50
+```
+
+
+---
+
+## 3. Upload to Hub
+
+```bash
+uv run python robometer/utils/upload_to_hub.py \
+  --model_dir ./logs/robometer4b_lora_robofac/checkpoint-500 \
+  --hub_model_id aliangdw/robometer-4b-lora-robofac \
+  --base_model "Qwen/Qwen3-VL-4B-Instruct" \
+  --commit_message "LoRA fine-tune on RoboFAC"
+```
+
+Or enable `logging.save_best.upload_to_hub: true` in config for upload during training.
+
+---
+
+## 4. Inference
+
+```bash
+uv run python scripts/example_inference_local.py \
+  --model-path aliangdw/robometer-4b-lora-robofac \
+  --video /path/to/video.mp4 \
+  --task "Insert the cylinder"
+```
+
+Server: `uv run python robometer/evals/eval_server.py ... model_path=aliangdw/robometer-4b-lora-robofac`. Eval: `run_baseline_eval.py` with `reward_model=rbm`, `model_path=...` (see [README](README.md)).
+
+---
+
+## 5. Baseline: Fine-tune from base Qwen-VL (no Robometer checkpoint)
+
+For comparison, run the same `train.py` on the same data but **without** loading a Robometer checkpoint. Training starts from the base Qwen-VL plus randomly initialized progress/preference heads.
+
+```bash
+export ROBOMETER_PROCESSED_DATASETS_PATH=/path/to/your/processed_datasets
+
+uv run python train.py \
+  model.base_model_id=Qwen/Qwen3-VL-4B-Instruct \
+  model.use_peft=true \
+  model.train_progress_head=true \
+  model.train_preference_head=true \
+  data.train_datasets=[aliangdw_robofac_rbm_robofac] \
+  data.eval_datasets=[aliangdw_robofac_rbm_robofac] \
+  training.per_device_train_batch_size=8 \
+  training.learning_rate=2e-5 \
+  training.warmup_ratio=0.1 \
+  training.weight_decay=0.01 \
+  training.max_steps=1000 \
+  training.output_dir=./logs \
+  training.exp_name=qwen3vl_lora_robofac_baseline \
+  logging.log_to=[wandb] \
+  custom_eval.reward_alignment=[aliangdw_robofac_rbm_robofac] \
+  custom_eval.policy_ranking=[aliangdw_robofac_rbm_robofac] \
+  logging.save_best.metric_names=[eval_rew_align/pearson_robofac,eval_p_rank/kendall_last_robofac] \
+  logging.save_best.greater_is_better=[true,true] \
+  training.eval_steps=50 \
+  training.custom_eval_steps=50
+
+uv run python train.py \
+  model.base_model_id=Qwen/Qwen3-VL-4B-Instruct \
+  model.use_peft=false \
+  model.train_progress_head=true \
+  model.train_preference_head=true \
+  data.train_datasets=[aliangdw_robofac_rbm_robofac] \
+  data.eval_datasets=[aliangdw_robofac_rbm_robofac] \
+  training.per_device_train_batch_size=8 \
+  training.learning_rate=2e-5 \
+  training.warmup_ratio=0.1 \
+  training.weight_decay=0.01 \
+  training.max_steps=1000 \
+  training.output_dir=./logs \
+  training.exp_name=qwen3vl_robofac_baseline \
+  logging.log_to=[wandb] \
+  custom_eval.reward_alignment=[aliangdw_robofac_rbm_robofac] \
+  custom_eval.policy_ranking=[aliangdw_robofac_rbm_robofac] \
+  logging.save_best.metric_names=[eval_rew_align/pearson_robofac,eval_p_rank/kendall_last_robofac] \
+  logging.save_best.greater_is_better=[true,true] \
+  training.eval_steps=50 \
+  training.custom_eval_steps=50
+```

robometer/README.md

@@ -0,0 +1,267 @@
+# Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons
+
+[![arXiv](https://img.shields.io/badge/arXiv-2603.02115-b31b1b.svg)](https://arxiv.org/abs/2603.02115)
+[![GitHub](https://img.shields.io/badge/GitHub-robometer-181717?logo=github)](https://github.com/robometer/robometer)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![Model](https://img.shields.io/badge/Model-FFD21E?logo=huggingface)](https://huggingface.co/aliangdw/Robometer-4B)
+[![Dataset](https://img.shields.io/badge/Dataset-RBM--1M-FFD21E?logo=huggingface)](https://huggingface.co/datasets/)
+[![RBM-1M Visualizer](https://img.shields.io/badge/Visualizer-RBM--FFD21E?logo=huggingface)](https://huggingface.co/spaces/rewardfm/visualizer)
+[![RewardEval UI](https://img.shields.io/badge/%20RewardEval%20UI-FFD21E?logo=huggingface)](https://huggingface.co/spaces/rewardfm/rewardeval_ui)
+
+<p align="center">
+  <img src="assets/robometer.jpg" alt="Robometer" width="100%"/>
+</p>
+
+---
+
+## Abstract
+
+General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations, providing only local, frame-level supervision. While effective for expert demonstrations, this paradigm scales poorly to large-scale robotics datasets where failed and suboptimal trajectories are abundant and assigning dense progress labels is ambiguous. We introduce **Robometer**, a scalable reward modeling framework that combines intra-trajectory progress supervision with inter-trajectory preference supervision. Robometer is trained with a dual objective: a frame-level progress loss that anchors reward magnitude on expert data, and a trajectory-comparison preference loss that imposes global ordering constraints across trajectories of the same task, enabling effective learning from both real and augmented failed trajectories. To support this formulation at scale, we curate **RBM-1M**, a reward-learning dataset comprising over one million trajectories spanning diverse robot embodiments and tasks, including substantial suboptimal and failure data. Across benchmarks and real-world evaluations, Robometer learns more generalizable reward functions than prior methods and improves robot learning performance across a diverse set of downstream applications.
+
+---
+
+## 📦 Package structure
+
+```
+robometer/
+├── robometer/              # Main package
+│   ├── data/               # Datasets and preprocessing
+│   ├── configs/            # Hydra and experiment configs
+│   ├── models/             # Model definitions
+│   └── evals/              # Baseline evals (GVL, VLAC, Robodopamine, etc.)
+├── eval_commands/          # Shell scripts for baseline evals
+├── train.py                # Training entrypoint
+└── pyproject.toml          # Dependencies (uv)
+```
+
+---
+
+## 🛠️ Setup
+
+### Prerequisites
+
+- Git, Python 3.10+
+- NVIDIA drivers (GPU)
+- [uv](https://github.com/astral-sh/uv#installation) (recommended)
+
+### Install (main env)
+
+```bash
+git clone https://github.com/aliang8/robometer.git
+cd robometer
+
+# Create venv and install
+uv sync
+```
+
+### Dataset setup
+
+```bash
+hf auth
+export ROBOMETER_PROCESSED_DATASETS_PATH=/path/to/save/processed_datasets
+./scripts/download_processed_datasets.sh
+./scripts/untar_processed_datasets.sh
+```
+
+For raw download and preprocessing, see [📥 Download raw datasets](#-download-raw-datasets-optional) below.
+
+---
+
+## 🔍 Inference
+
+Inference runs a **pretrained RBM model** on your own videos to get per-frame progress, per-frame success, and (for two trajectories) preference scores.
+
+**Pretrained models (Hugging Face):**
+
+- **[Robometer-4B](https://huggingface.co/robometer/Robometer-4B)** — general-purpose, trained on RBM-1M
+- ~~**Robometer-4B-LIBERO** — LIBERO-10 / Spatial / Object / Goal~~ removed because the standard Robometer model is already trained on LIBERO 10/Spatial/Object/Goal+failures and simply performs better than the version trained exclusively on LIBERO
+
+### Inference via HTTP server
+
+Start the eval server on your machine, then call it with a video and task:
+
+```bash
+uv run python robometer/evals/eval_server.py \
+  server_url=0.0.0.0 \
+  server_port=8000
+```
+
+Then run the client (no robometer dependency):
+
+```bash
+# SOAR
+uv run python scripts/example_inference.py \
+  --eval-server-url http://localhost:8000 \
+  --video scripts/example_videos/soar_put_green_stick_in_brown_bowl.mp4 \
+  --task "Put green stick in brown bowl" \
+  --fps 3
+
+# Berkeley RPT (Wrist)
+uv run python scripts/example_inference.py \
+  --eval-server-url http://localhost:8000 \
+  --video scripts/example_videos/berkeley_rpt_stack_cup.mp4 \
+  --task "Pick up the yellow cup and stack it on the other cup" \
+  --fps 3
+
+# Your own video
+uv run python scripts/example_inference.py \
+  --eval-server-url http://localhost:8000 \
+  --video /path/to/video.mp4 \
+  --task "your task description"
+```
+
+To run the model locally (loads checkpoint from Hugging Face, no server):
+
+```bash
+uv run python scripts/example_inference_local.py \
+  --model-path aliangdw/Robometer-4B \
+  --video /path/to/video.mp4 \
+  --task "your task description"
+```
+
+Use `aliangdw/Robometer-4B-LIBERO` for the LIBERO-finetuned model.
+
+---
+
+## 🏋️ Training
+
+### Training
+
+**Train on RBM-1M in-distribution and evaluate on RBM-1M-OOD**
+
+```bash
+uv run accelerate launch --config_file robometer/configs/distributed/fsdp.yaml train.py \
+  data.train_datasets=[rbm-1m-id] \
+  data.eval_datasets=[rbm-1m-ood] \
+  data.max_frames=4 \
+  model.train_progress_head=true \
+  model.train_preference_head=true \
+  training.max_steps=5000 \
+  custom_eval.reward_alignment=[rbm-1m-ood] \
+  custom_eval.policy_ranking=[rbm-1m-ood] \
+  custom_eval.confusion_matrix=[rbm-1m-ood]
+```
+
+**LIBERO: train on 10 / object / spatial / goal, test on 90.**
+
+```bash
+uv run accelerate launch --config_file robometer/configs/distributed/fsdp.yaml train.py \
+  data.train_datasets=[libero_pi0] \
+  data.eval_datasets=[mw] \
+  data.max_frames=4 \
+  model.train_progress_head=true \
+  model.train_preference_head=true \
+  training.max_steps=5000 \
+  custom_eval.reward_alignment=[libero_pi0] \
+  custom_eval.policy_ranking=[libero_pi0]
+```
+
+See `robometer/configs/experiment_configs.py` for more config options.
+
+---
+
+## 🔧 LoRA fine-tune Robometer for new dataset
+
+Preprocess a new dataset, LoRA fine-tune from **Robometer-4B** on your own data, upload the model to the Hub, and run inference:
+
+- **Preprocessing:** Add your dataset to the preprocess config and run the preprocessor; for raw videos (e.g. [MINT-SJTU/RoboFAC-dataset](https://huggingface.co/datasets/MINT-SJTU/RoboFAC-dataset)), convert to RBM format first via `dataset_upload`, then preprocess.
+- **Fine-tuning:** Set `model.use_peft=true` and `training.resume_from_checkpoint=aliangdw/Robometer-4B`, then train on your dataset.
+- **Upload & inference:** Use `robometer/utils/upload_to_hub.py` to push checkpoints; run `scripts/example_inference_local.py` with your Hub model.
+
+Full step-by-step: **[FINETUNE_ROBOMETER.md](FINETUNE_ROBOMETER.md)**.
+
+---
+
+## 📊 Evaluation
+
+Evaluation runs **benchmark evals** (reward alignment, policy ranking, confusion matrix) on fixed datasets to measure model quality. Use this to reproduce paper results or compare checkpoints.
+
+### Robometer evaluation
+
+Run RBM with `reward_model=rbm`; override `model_path` and `custom_eval.*` as needed. See `eval_commands/*.sh` for ReWIND, Robo-Dopamine, VLAC, RoboReward.
+
+**Reward alignment**
+
+```bash
+uv run python robometer/evals/run_baseline_eval.py \
+    reward_model=rbm \
+    model_path=aliangdw/Robometer-4B \
+    custom_eval.eval_types=[reward_alignment] \
+    custom_eval.reward_alignment=[rbm-1m-id,rbm-1m-ood] \
+    custom_eval.use_frame_steps=true \
+    custom_eval.subsample_n_frames=5 \
+    custom_eval.reward_alignment_max_trajectories=30 \
+    max_frames=4 \
+    model_config.batch_size=32
+```
+
+**Policy ranking**
+
+```bash
+uv run python robometer/evals/run_baseline_eval.py \
+    reward_model=rbm \
+    model_path=aliangdw/Robometer-4B \
+    custom_eval.eval_types=[policy_ranking] \
+    custom_eval.policy_ranking=[rbm-1m-ood] \
+    custom_eval.use_frame_steps=false \
+    custom_eval.num_examples_per_quality_pr=1000 \
+    max_frames=4 \
+    model_config.batch_size=32
+```
+
+**Confusion matrix**
+
+```bash
+uv run python robometer/evals/run_baseline_eval.py \
+    reward_model=rbm \
+    model_path=aliangdw/Robometer-4B \
+    custom_eval.eval_types=[confusion_matrix] \
+    custom_eval.confusion_matrix=[[aliangdw_usc_franka_policy_ranking_usc_franka_policy_ranking,jesbu1_utd_so101_clean_policy_ranking_top_utd_so101_clean_policy_ranking_top,aliangdw_usc_xarm_policy_ranking_usc_xarm_policy_ranking]] \
+    max_frames=4 \
+    model_config.batch_size=32
+```
+
+Details: [robometer/evals/README.md](robometer/evals/README.md).
+
+### Baseline evaluation (all models)
+
+- **RBM:** use the [reward alignment](#robometer-evaluation), [policy ranking](#robometer-evaluation), or [confusion matrix](#robometer-evaluation) commands above; set `model_path` to your checkpoint.
+- **ReWIND, Robo-Dopamine, VLAC, RoboReward:** see [robometer/evals/README.md](robometer/evals/README.md) and `eval_commands/reward_alignment.sh`, `eval_commands/policy_ranking.sh`, `eval_commands/confusion_matrix.sh`. For Robo-Dopamine use `.venv-robodopamine/bin/python` (vLLM) instead of `uv run`.
+
+---
+
+## 📊 Dataset generation
+
+Supported: **AgiBotWorld** (streaming), **LIBERO** (HDF5), and custom configs.
+
+```bash
+# AgiBotWorld
+uv run python dataset_upload/generate_hf_dataset.py --config_path=dataset_upload/configs/data_gen_configs/agibot_world.yaml
+
+# LIBERO
+uv run python dataset_upload/generate_hf_dataset.py --config_path=dataset_upload/configs/data_gen.yaml \
+  --dataset.dataset_path=LIBERO/libero/datasets/libero_90 --dataset.dataset_name=libero_90
+```
+
+See dataset_upload README and dataset_guides for adding datasets.
+
+---
+
+## 📥 Download raw datasets (optional)
+
+If you prefer not to use the processed datasets:
+
+```bash
+export ROBOMETER_DATASET_PATH=/path/to/your/robometer_dataset
+./scripts/download_data.sh
+
+# Preprocess
+uv run python -m robometer.data.scripts.preprocess_datasets --config robometer/configs/preprocess.yaml
+export ROBOMETER_PROCESSED_DATASETS_PATH=/path/to/save/processed_datasets
+```
+
+---
+
+## 📑 License
+
+This project is licensed under the [MIT License](LICENSE).

robometer/baseline_eval_output/rbm_smoke_10/all_metrics.json

@@ -0,0 +1,6 @@
+{
+  "reward_alignment": {
+    "racer_val/loss": 0.04008283880995868,
+    "racer_val/pearson": 0.9235551377640896
+  }
+}

robometer/baseline_eval_output/rbm_smoke_10/reward_alignment/metrics.json

@@ -0,0 +1,4 @@
+{
+  "racer_val/loss": 0.04008283880995868,
+  "racer_val/pearson": 0.9235551377640896
+}

robometer/baseline_eval_output/rbm_smoke_10/reward_alignment/racer_val_results.json

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robometer/dataset_upload/README.md

@@ -0,0 +1,224 @@
+# Adding New Datasets to Robometer
+
+This guide explains how to add new datasets to the Robometer training pipeline.
+
+## Supported Datasets
+
+### Ready-to-Use Datasets
+- **LIBERO**: Built-in HDF5 support → [📖 LIBERO Guide](dataset_guides/LIBERO.md)
+- **AgiBotWorld**: ✅ Native streaming support → [📖 AgiBotWorld Guide](dataset_guides/AgiBotWorld.md)
+
+### Custom Datasets
+- **Add Your Own**: DROID, Bridge, or any custom dataset → [📖 Custom Dataset Guide](dataset_guides/CustomDataset.md)
+
+## Quick Start
+
+### Use Existing Datasets
+```bash
+# AgiBotWorld (streaming)
+uv run python dataset_upload/generate_hf_dataset.py --config_path=dataset_upload/configs/data_gen_configs/agibot_world.yaml
+
+# LIBERO (local files)
+uv run python dataset_upload/generate_hf_dataset.py --config_path=dataset_upload/configs/data_gen.yaml
+```
+
+### Add Custom Dataset
+1. **Read the guide**: [Custom Dataset Guide](dataset_guides/CustomDataset.md)
+2. **Create loader**: `dataset_upload/{dataset_name}_loader.py`
+3. **Add config**: `dataset_upload/configs/data_gen_configs/{dataset_name}.yaml`
+4. **Test**: Run conversion and training
+
+## Architecture Overview
+
+Each dataset type has its own loader module. The main converter (`generate_hf_dataset.py`) is dataset-agnostic and works with any dataset-specific loader that follows the established interface.
+
+### Output Formats
+
+The converter supports two output formats:
+
+**Video Mode** (`--output.use_video=true`):
+- Creates MP4 video files using H.264 encoding
+- Videos are stored in organized directories: `trajectory_XXXX/trajectory.mp4`
+- Uses `datasets.Video()` feature for proper HuggingFace video display
+- Supports configurable frame rate, resolution, and cropping
+
+**Frame Mode** (`--output.use_video=false`):
+- Creates individual JPG image files
+- Images are stored in organized directories: `trajectory_XXXX/frame_XX.jpg`
+- Uses `datasets.Sequence(datasets.Image())` feature for image galleries
+- Supports configurable frame count and resolution
+
+### Video Processing Features
+
+The dataset converter includes built-in video processing:
+- **📹 Automatic Resizing**: Videos resized to consistent dimensions (configurable shortest edge size)
+- **⏱️ Frame Interpolation**: Downsamples to configurable frame count (default: all frames preserved)
+- **🎬 MP4 Creation**: Creates H.264 encoded MP4 files for optimal HuggingFace compatibility
+- **🎯 Quality Preservation**: Maintains visual quality while standardizing format
+- **📁 File Organization**: Organizes videos in trajectory-specific directories
+
+## Dataset Structure Requirements
+
+Your dataset loader must produce trajectories in the following format:
+
+```python
+{
+    'frames': Union[str, List[str]],   # Video file path (video mode) or list of image file paths (frame mode)
+    'actions': np.ndarray,             # Actions 
+    'is_robot': bool,                  # Whether this is robot data (True) or human data (False)
+    'task': str,                       # Human-readable task description
+    'optimal': str                     # Whether this trajectory is optimal
+}
+```
+
+**Note**: The dataset converter automatically creates MP4 video files or individual frame images based on the `use_video` setting.
+
+## Step-by-Step Guide
+
+### 1. Create Your Dataset Loader
+
+Create a new Python file in the `data/` directory following the naming convention: `{dataset_name}_loader.py`
+
+Example: `droid_loader.py` or `bridge_loader.py`
+
+```python
+#!/usr/bin/env python3
+"""
+DROID dataset loader for the generic dataset converter for Robometer model training.
+This module contains DROID-specific logic for loading and processing data.
+"""
+
+import os
+import numpy as np
+from typing import List, Dict
+from pathlib import Path
+from tqdm import tqdm
+
+def load_droid_dataset(base_path: str) -> Dict[str, List[Dict]]:
+    """Load DROID dataset and organize by task.
+    
+    Args:
+        base_path: Path to the DROID dataset directory
+        
+    Returns:
+        Dictionary mapping task names to lists of trajectory dictionaries
+    """
+    
+    print(f"Loading DROID dataset from: {base_path}")
+    
+    task_data = {}
+    
+    # Your dataset-specific loading logic here
+    # This is where you'll implement the logic to:
+    # 1. Find and read your data files
+    # 2. Extract frames, actions, rewards, etc.
+    # 3. Organize by task
+    # 4. Convert to the required format
+    
+    # Example structure (adapt to your dataset):
+    base_path = Path(base_path)
+    if not base_path.exists():
+        raise FileNotFoundError(f"DROID dataset path not found: {base_path}")
+    
+    # Find your data files (adapt this to your dataset structure)
+    data_files = list(base_path.glob("**/*.hdf5"))  # or whatever format you use
+    
+    for file_path in tqdm(data_files, desc="Processing DROID dataset"):
+        task_name = file_path.stem
+        
+        # Load your data file
+        # This is where you'll implement your specific loading logic
+        trajectories = load_trajectories_from_file(file_path)
+        
+        task_data[task_name] = trajectories
+    
+    print(f"Loaded {sum(len(trajectories) for trajectories in task_data.values())} trajectories from {len(task_data)} tasks")
+    return task_data
+
+def load_trajectories_from_file(file_path: Path) -> List[Dict]:
+    """Load trajectories from a single DROID data file."""
+    trajectories = []
+    
+    # Implement your file loading logic here
+    # This will depend on your dataset format (HDF5, JSON, pickle, etc.)
+    
+    # Example for HDF5 format:
+    import h5py
+    with h5py.File(file_path, 'r') as f:
+        # Navigate your HDF5 structure and extract data
+        # Convert to the required format
+        pass
+    
+    return trajectories
+```
+
+### 2. Update the Main Converter
+
+Add your dataset type to the main converter in `generate_hf_dataset.py`:
+
+```python
+# In the main() function, add your dataset type:
+elif cfg.dataset.dataset_type == "droid":
+    from dataset_loaders.droid_loader import load_droid_dataset
+    # Load the trajectories using your loader
+    task_data = load_droid_dataset(cfg.dataset.dataset_path)
+    trajectories = flatten_task_data(task_data)
+```
+
+### 3. Test Your Loader
+
+Create a simple test script to verify your loader works:
+
+```python
+# test_droid_loader.py
+from droid_loader import load_droid_dataset
+from helpers import flatten_task_data
+
+# Test your loader
+task_data = load_droid_dataset("/path/to/your/droid/dataset")
+trajectories = flatten_task_data(task_data)
+
+print(f"Loaded {len(trajectories)} trajectories")
+print(f"Sample trajectory keys: {list(trajectories[0].keys())}")
+print(f"Sample task: {trajectories[0].get('task', 'No task found')}")
+```
+
+### 4. Run Dataset Conversion
+
+Use the main converter with your new dataset:
+
+```bash
+uv run python data/generate_hf_dataset.py \
+    --config_path=configs/dataset_.yaml \
+    --dataset.dataset_name=your_dataset \
+    --dataset.dataset_path=/path/to/your/dataset \
+    --output.output_dir=your_robometer_dataset \
+    --output.max_trajectories=1000 \
+    --output.max_frames=-1 \
+    --output.use_video=true \
+    --output.fps=10 \
+    --output.shortest_edge_size=240 \
+    --output.center_crop=false
+```
+
+### Visualize the Dataset
+
+```bash
+uv run python visualize_dataset.py --dataset_path=your_robometer_dataset/your_dataset_name
+```
+
+## Dataset-Specific Guides
+
+📁 **[Browse All Dataset Guides](dataset_guides/)** - Complete overview with quick reference table
+
+### Individual Guides
+- **[📖 AgiBotWorld Guide](dataset_guides/AgiBotWorld.md)** - Streaming support, webdataset format
+- **[📖 LIBERO Guide](dataset_guides/LIBERO.md)** - HDF5 files, simulation data  
+- **[📖 Custom Dataset Guide](dataset_guides/CustomDataset.md)** - Add DROID, Bridge, or your own dataset
+
+Each guide includes:
+- ✅ Prerequisites and setup
+- ✅ Configuration examples
+- ✅ Troubleshooting tips
+- ✅ Performance notes
+- ✅ Integration with Robometer training

robometer/dataset_upload/__init__.py

@@ -0,0 +1 @@
+

robometer/dataset_upload/configs/data_gen.yaml

@@ -0,0 +1,17 @@
+# config.yaml
+dataset:
+  dataset_path: LIBERO/libero/datasets/libero_90
+  dataset_name: libero_90  # This will be the dataset name / data source
+
+output:
+  output_dir: robometer_dataset
+  max_trajectories: -1
+  max_frames: 64
+  use_video: true
+  fps: 30
+  shortest_edge_size: 240
+  center_crop: false
+
+hub:
+  push_to_hub: true
+  hub_repo_id: aliangdw/robometer

robometer/dataset_upload/configs/data_gen_configs/agibot_world.yaml

@@ -0,0 +1,15 @@
+# AgiBotWorld dataset configuration
+dataset:
+  dataset_path: "agibot-world/AgiBotWorld-Alpha"  # HuggingFace dataset name for streaming
+  dataset_name: agibotworld
+
+output:
+  output_dir: datasets/agibotworld_dataset
+  max_trajectories: 100  # Limit for testing, increase for full dataset
+  max_frames: 64
+  use_video: true
+  fps: 30
+
+hub:
+  push_to_hub: true  # Set to true when ready to upload
+  hub_repo_id: agibotworld_rfm

robometer/dataset_upload/configs/data_gen_configs/autoeval.yaml

@@ -0,0 +1,20 @@
+# config.yaml
+dataset:
+  dataset_path: ./datasets/auto_eval  # Root containing eval_data/
+  dataset_name: auto_eval_rfm
+
+output:
+  output_dir: ./robometer_dataset/auto_eval_rfm
+  max_trajectories: -1
+  max_frames: 64
+  use_video: true
+  fps: 5
+  shortest_edge_size: 240
+  center_crop: false
+  num_workers: 4
+
+hub:
+  push_to_hub: true
+  hub_repo_id: auto_eval_rfm
+
+

robometer/dataset_upload/configs/data_gen_configs/egocot.yaml

@@ -0,0 +1,17 @@
+# config.yaml
+dataset:
+  dataset_path: ./egocot # Path to EgoCoT dataset directory that has EgoCOT_clear/ and results.json inside
+  dataset_name: egocot # This will be the dataset name / data source
+
+output:
+  output_dir: ./robometer_dataset/egocot_rfm
+  max_trajectories: -1  # -1 for all trajectories
+  max_frames: 8  # EgoCoT uses 8 consecutive frames anyway
+  use_video: true
+  fps: 1  # Since EgoCoT is not a video dataset, we use 1 fps
+  shortest_edge_size: 224  # Standard size for vision models
+  center_crop: false
+
+hub:
+  push_to_hub: true
+  hub_repo_id: egocot_rfm

robometer/dataset_upload/configs/data_gen_configs/egodex.yaml

@@ -0,0 +1,18 @@
+# EgoDex dataset configuration
+dataset:
+  dataset_path: "~/part2"  # Local path to EgoDex dataset directory
+  dataset_name: egodex_part2
+
+output:
+  output_dir: robometer_dataset/egodex_rfm
+  max_trajectories: -1
+  max_frames: 32
+  shortest_edge_size: 240
+  use_video: true
+  fps: 5
+  center_crop: false
+  num_workers: 3
+
+hub:
+  push_to_hub: true 
+  hub_repo_id: egodex_rfm

robometer/dataset_upload/configs/data_gen_configs/epic.yaml

@@ -0,0 +1,20 @@
+# config.yaml
+dataset:
+  dataset_path: ../epic_kitchens_100
+  dataset_name: epic
+
+output:
+  output_dir: ./robometer_dataset/epic_rfm
+  max_trajectories: -1
+  max_frames: 64
+  use_video: true
+  fps: 30
+  shortest_edge_size: 240
+  center_crop: false
+  num_workers: 3 # less workers because cv2/ffmpeg loads are multiprocessed
+
+hub:
+  push_to_hub: true
+  hub_repo_id: epic_rfm
+
+

robometer/dataset_upload/configs/data_gen_configs/failsafe.yaml

@@ -0,0 +1,20 @@
+# config.yaml
+dataset:
+  dataset_path: ./datasets/failsafe_data  # Root containing FailPickCube-v1/ FailPushCube-v1/ FailStackCube-v1/ and jsons
+  dataset_name: failsafe
+
+output:
+  output_dir: ./robometer_dataset/failsafe_rfm
+  max_trajectories: -1
+  max_frames: 64
+  use_video: true
+  fps: 10
+  shortest_edge_size: 240
+  num_workers: 4
+  center_crop: false
+
+hub:
+  push_to_hub: true
+  hub_repo_id: failsafe_rfm
+
+

robometer/dataset_upload/configs/data_gen_configs/fino_net.yaml

@@ -0,0 +1,18 @@
+# config.yaml for FinoNet dataset
+dataset:
+  dataset_path: ./datasets/fino-net # Path to the dataset root (contains failure.zip or unzipped failure directory)
+  dataset_name: fino_net  # This will be the dataset name / data source
+
+output:
+  output_dir: robometer_dataset/fino_net_rfm
+  max_trajectories: -1  # -1 for all episodes
+  max_frames: 64
+  use_video: true
+  fps: 5
+  shortest_edge_size: 240
+  center_crop: false
+
+hub:
+  push_to_hub: true
+  hub_repo_id: fino_net_rfm
+

robometer/dataset_upload/configs/data_gen_configs/galaxea.yaml

@@ -0,0 +1,20 @@
+# config.yaml
+dataset:
+  dataset_path: ./datasets/galaxea # Root containing 'rlds/'
+  dataset_name: galaxea_part1_r1_lite
+
+output:
+  output_dir: ./robometer_dataset/galaxea_rfm
+  max_trajectories: -1
+  max_frames: 64
+  use_video: true
+  fps: 15
+  shortest_edge_size: 240
+  center_crop: false
+  num_workers: 1
+
+hub:
+  push_to_hub: true
+  hub_repo_id: galaxea_rfm
+
+

robometer/dataset_upload/configs/data_gen_configs/h2r.yaml

@@ -0,0 +1,17 @@
+# config.yaml
+dataset:
+  dataset_path: ./humanandrobot/data/v1
+  dataset_name: h2r  # This will be the dataset name / data source
+
+output:
+  output_dir: datasets/h2r_rfm
+  max_trajectories: -1
+  max_frames: 64
+  use_video: true
+  fps: 10
+  shortest_edge_size: 240
+  center_crop: false
+
+hub:
+  push_to_hub: true
+  hub_repo_id: h2r_rfm

robometer/dataset_upload/configs/data_gen_configs/hand_paired.yaml

@@ -0,0 +1,19 @@
+# config.yaml
+dataset:
+  dataset_path: ./datasets/hand_paired_data
+  dataset_name: hand_paired
+
+output:
+  output_dir: ./robometer_dataset/hand_paired_rfm
+  max_trajectories: -1
+  max_frames: 64
+  use_video: true
+  fps: 10
+  shortest_edge_size: 240
+  center_crop: false
+  num_workers: 4
+
+hub:
+  push_to_hub: true
+  hub_repo_id: hand_paired_rfm
+

robometer/dataset_upload/configs/data_gen_configs/humanoid_everyday.yaml

@@ -0,0 +1,18 @@
+# Humanoid Everyday dataset configuration
+dataset:
+  dataset_path: "./datasets/humanoid_everyday"  # Path containing zip files with humanoid everyday datasets
+  dataset_name: humanoid_everyday_rfm
+
+output:
+  output_dir: ./robometer_dataset/humanoid_everyday_rfm
+  max_trajectories: -1
+  max_frames: 64
+  use_video: true
+  fps: 30
+  shortest_edge_size: 240
+  center_crop: false
+  num_workers: 4
+
+hub:
+  push_to_hub: true
+  hub_repo_id: humanoid_everyday_rfm