Release the submitted RoboEval CoRL 2026 policy

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: apache-2.0
+pipeline_tag: robotics
+library_name: openpi
+tags:
+  - robotics
+  - openpi
+  - pi0.5
+  - vision-language-action
+  - reinforcement-learning
+  - bimanual-manipulation
+  - roboeval
+datasets:
+  - helen9975/roboeval_ee_delta_20hz_scaled
+---
+
+# RoboEval CoRL 2026 solution: off-policy RL with online inference tuning
+
+This is the policy I submitted to the [RoboEval Challenge at CoRL 2026](https://robo-eval.github.io/challenge.html):
+one modified π0.5 for eight bimanual tasks with two Franka arms, in simulation. The weights are an average of two
+checkpoints of the same run, 0.65 × step 48250 + 0.35 × step 58000.
+
+- Code: https://github.com/IliaLarchenko/RoboEvalSolution
+- Report: https://arxiv.org/abs/2611.11464
+- Project page: https://ilialarchenko.com/projects/roboeval2026/
+
+## Results
+
+Scored with the official RoboEval Diagnostic Score (RDS), each task with its final inference settings.
+
+| | Episodes per task | RDS | Success |
+|---|---:|---:|---:|
+| Official seeds (1000–1049) | 50 | **0.987** | 99.25% |
+| Held-out random seeds | 500 | **0.990** | 99.60% |
+
+| Task | Official success | Official RDS | Held-out success | Held-out RDS |
+|---|---:|---:|---:|---:|
+| LiftPot | 1.000 | 1.000 | 1.000 | 0.999 |
+| LiftTray | 0.980 | 0.984 | 0.998 | 0.998 |
+| StackTwoBlocks | 1.000 | 0.977 | 0.996 | 0.979 |
+| StackSingleBookShelf | 1.000 | 1.000 | 0.998 | 0.998 |
+| CubeHandover | 1.000 | 1.000 | 1.000 | 0.998 |
+| PackBox | 1.000 | 0.969 | 1.000 | 0.974 |
+| PickSingleBookFromTable | 0.960 | 0.967 | 0.988 | 0.988 |
+| RotateValve | 1.000 | 1.000 | 0.988 | 0.989 |
+
+A 50-episode run varies by about ±0.005 RDS, so the two rows agree within noise. I made every decision on held-out
+seeds and never tuned on the official ones.
+
+## Inputs and outputs
+
+- **Inputs:** five RGB cameras at 256×256 (head, front, external, left wrist, right wrist), proprioception (joint
+  positions and velocities, gripper states) and a task id. There is no text prompt. Some tasks hide some of these
+  inputs at inference.
+- **Actions:** chunks of 30 steps (1.5 s at 20 Hz) of 14-D end-effector actions for both arms: per arm a 3-D position,
+  a 3-D rotation vector and a gripper command, relative to the commanded pose at the chunk start. They convert to the
+  challenge's end-effector delta actions.
+- **Tasks:** LiftPot, LiftTray, StackTwoBlocks, StackSingleBookShelf, CubeHandover, PackBox, PickSingleBookFromTable
+  and RotateValve, all on the PositionAndOrientation variation.
+
+## How to run it
+
+The policy runs through the code repository, not through `transformers`. You need a Linux machine with an NVIDIA GPU
+(driver >= 580 for the default CUDA 13 build; on older drivers use `bash setup.sh --cuda12`).
+
+```bash
+git clone --recurse-submodules https://github.com/IliaLarchenko/RoboEvalSolution.git && cd RoboEvalSolution
+bash setup.sh    # .venv (policy) and .venv-sim (simulator)
+.venv/bin/hf download IliaLarchenko/RoboEvalSolution --local-dir ../checkpoint
+
+CUDA_VISIBLE_DEVICES=0 .venv/bin/python scripts/serve_policy.py --config-name pi05_roboeval \
+    --checkpoint-dir ../checkpoint --num-candidates 6 &
+MUJOCO_GL=egl .venv-sim/bin/python scripts/rollout.py --server ws://localhost:8765 --out ../runs/eval_x \
+    --episodes-per-task 50 --workers 20 --best configs/settings.json
+```
+
+`rollout.py` builds the environments like the official evaluator and writes the official scores to `summary.json`. It
+refuses the official seeds 1000–1049. Stop the server afterwards with `kill %1`.
+
+## Inference settings
+
+The settings matter a lot and differ per task: the number of flow steps, noise temperature, CFG scale, best-of-N
+ranked by the Q head, how many actions to execute and carry over, execution speed, gripper threshold and hidden
+inputs. They are in
+[`configs/settings.json`](https://github.com/IliaLarchenko/RoboEvalSolution/blob/main/configs/settings.json).
+Without them you won't reproduce the scores above.
+
+## Training
+
+The model is π0.5 (a PaliGemma VLM plus a flow-matching action expert) with a value query, a Q head and advantage
+conditioning, so it is also its own value function. I trained it in an off-policy RL loop with advantages of the exact
+challenge score (AWR, and advantage conditioning inspired by RECAP). Every rollout was also a Thompson-sampling trial
+of about 20 inference parameters. Training used about 600 GPU-hours.
+
+Data:
+
+- the organisers' [RoboEval demonstrations](https://huggingface.co/datasets/helen9975/roboeval_ee_delta_20hz_scaled);
+- my own scripted demonstrations with full reward, generated in simulation from privileged state;
+- more than 240k of the policy's own rollouts, plus recovery episodes that continue from its failures.
+
+The only external pretraining is the public π0.5 base model.
+
+## Limitations
+
+- It was trained and evaluated in simulation only and has never run on a real robot.
+- It is tuned to these eight tasks, this robot, these cameras and this exact score. Don't expect it to work elsewhere
+  without retraining.
+- Part of the score comes from per-task inference tuning, which is slow and would be hard to justify in a real
+  deployment.
+
+## License
+
+Code and weights are released under Apache-2.0.
+
+## Citation
+
+```bibtex
+@misc{larchenko2026roboeval,
+  title  = {Off-Policy RL with Online Inference Tuning: My Solution to the CoRL 2026 RoboEval Challenge},
+  author = {Larchenko, Ilia},
+  year   = {2026},
+  eprint = {2611.11464},
+  archivePrefix = {arXiv},
+  primaryClass = {cs.RO},
+  url    = {https://arxiv.org/abs/2611.11464}
+}
+```
+
+The benchmark, simulator and demonstrations are from [RoboEval](https://robo-eval.github.io)
+([Wang et al., 2025](https://arxiv.org/abs/2507.00435)). The base model is π0.5 by
+[Physical Intelligence](https://github.com/Physical-Intelligence/openpi).

assets/roboeval/fast_tokenizer/metadata.json

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+{
+ "vocab_size": 1024,
+ "scale": 10.0,
+ "encoded_dim_ranges": [
+  [
+   0,
+   14
+  ]
+ ],
+ "action_horizon": 30,
+ "num_training_chunks": 100000,
+ "tokens_mean": 60.9905,
+ "tokens_p99": 111.0,
+ "tokens_max": 125
+}

assets/roboeval/fast_tokenizer/processing_action_tokenizer.py

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+import logging
+from typing import ClassVar
+
+import numpy as np
+from scipy.fft import dct
+from scipy.fft import idct
+from tokenizers import ByteLevelBPETokenizer
+from tokenizers.trainers import BpeTrainer
+from transformers import PreTrainedTokenizerFast
+from transformers.processing_utils import ProcessorMixin
+
+
+class UniversalActionProcessor(ProcessorMixin):
+    attributes: ClassVar[list[str]] = ["bpe_tokenizer"]
+    bpe_tokenizer_class: str = "AutoTokenizer"
+
+    def __init__(
+        self,
+        bpe_tokenizer: PreTrainedTokenizerFast,
+        scale: float = 10,
+        vocab_size: int = 1024,
+        min_token: int = 0,
+        *,
+        action_dim: int | None = None,
+        time_horizon: int | None = None,
+    ):
+        self.scale = scale
+        self.vocab_size = vocab_size
+        self.min_token = min_token
+
+        # Action horizon and dimension needed during decoding. These can be specified
+        # in three ways (in order of priority):
+        # 1. passed in as kwargs to decode()
+        # 2. in the constructor
+        # 3. cached from the last time decode() was called
+        self.time_horizon = time_horizon
+        self.action_dim = action_dim
+        self.called_time_horizon = time_horizon
+        self.called_action_dim = action_dim
+
+        super().__init__(bpe_tokenizer)
+
+    def __call__(self, action_chunk: np.array) -> np.array:
+        assert action_chunk.ndim <= 3, "Only 3 dimensions supported: [batch, timesteps, action_dim]"
+        if action_chunk.ndim == 2:
+            action_chunk = action_chunk[None, ...]
+
+        # Cache the time horizon and action dimension for decoding
+        self.called_time_horizon = action_chunk.shape[-2]
+        self.called_action_dim = action_chunk.shape[-1]
+
+        dct_coeff = dct(action_chunk, axis=1, norm="ortho")
+        dct_coeff = np.around(dct_coeff * self.scale)
+        tokens = []
+        for elem in dct_coeff:
+            token_str = "".join(map(chr, np.maximum(elem.flatten() - self.min_token, 0).astype(int)))
+            tokens.append(self.bpe_tokenizer(token_str)["input_ids"])
+        return tokens
+
+    def decode(
+        self,
+        tokens: list[list[int]],
+        *,
+        time_horizon: int | None = None,
+        action_dim: int | None = None,
+    ) -> np.array:
+        self.time_horizon = time_horizon or self.time_horizon or self.called_time_horizon
+        self.action_dim = action_dim or self.action_dim or self.called_action_dim
+
+        # Cache the time horizon and action dimension for the next call
+        self.called_time_horizon = self.time_horizon
+        self.called_action_dim = self.action_dim
+
+        assert (
+            self.time_horizon is not None and self.action_dim is not None
+        ), "Tokenizer not initialized, call encode() once or pass in time_horizon and action_dim."
+
+        decoded_actions = []
+        for token in tokens:
+            try:
+                decoded_tokens = self.bpe_tokenizer.decode(token)
+                decoded_dct_coeff = np.array(list(map(ord, decoded_tokens))) + self.min_token
+                decoded_dct_coeff = decoded_dct_coeff.reshape(-1, self.action_dim)
+                assert (
+                    decoded_dct_coeff.shape
+                    == (
+                        self.time_horizon,
+                        self.action_dim,
+                    )
+                ), f"Decoded DCT coefficients have shape {decoded_dct_coeff.shape}, expected ({self.time_horizon}, {self.action_dim})"
+            except Exception as e:
+                print(f"Error decoding tokens: {e}")
+                print(f"Tokens: {token}")
+                decoded_dct_coeff = np.zeros((self.time_horizon, self.action_dim))
+            decoded_actions.append(idct(decoded_dct_coeff / self.scale, axis=0, norm="ortho"))
+        return np.stack(decoded_actions)
+
+    @classmethod
+    def fit(
+        cls,
+        action_data: list[np.array],
+        scale: float = 10,
+        vocab_size: int = 1024,
+        *,
+        time_horizon: int | None = None,
+        action_dim: int | None = None,
+    ) -> "UniversalActionProcessor":
+        # Run DCT over all inputs
+        dct_tokens = [dct(a, axis=0, norm="ortho").flatten() for a in action_data]
+
+        # Quantize and find min token
+        max_token = int(np.around(np.concatenate(dct_tokens) * scale).max())
+        min_token = int(np.around(np.concatenate(dct_tokens) * scale).min())
+        min_vocab_size = max_token - min_token
+
+        assert (
+            min_vocab_size <= vocab_size
+        ), f"Vocab size {vocab_size} is too small for the range of tokens {min_vocab_size}"
+        if min_vocab_size + 100 > vocab_size:
+            logging.warning(
+                f"Initial alphabet size {min_vocab_size} is almost as large as the vocab"
+                f"size {vocab_size}, consider increasing vocab size"
+            )
+
+        # Make token iterator for BPE training
+        def _token_iter():
+            for tokens in dct_tokens:
+                rounded_tokens = np.around(tokens * scale) - min_token
+                rounded_tokens = rounded_tokens.astype(int)
+                string = "".join(map(chr, rounded_tokens))
+                yield string
+
+        # Train BPE tokenizer
+        bpe = ByteLevelBPETokenizer()
+
+        # Set up the entire range of possible tokens as the initial alphabet
+        alphabet = [chr(i) for i in range(max_token - min_token + 1)]
+        trainer = BpeTrainer(
+            vocab_size=vocab_size,
+            min_frequency=2,
+            show_progress=True,
+            special_tokens=[],
+            initial_alphabet=alphabet,
+            max_token_length=10000,
+        )
+
+        # Train the inner tokenizer (don't use ByteLevelBPETokenizer.train_from_iterator()
+        # because it doesn't support custom alphabets)
+        bpe._tokenizer.train_from_iterator(_token_iter(), trainer=trainer)
+
+        return cls(
+            PreTrainedTokenizerFast(tokenizer_object=bpe, clean_up_tokenization_spaces=False),
+            scale=scale,
+            vocab_size=vocab_size,
+            min_token=min_token,
+            time_horizon=time_horizon,
+            action_dim=action_dim,
+        )

assets/roboeval/fast_tokenizer/processor_config.json

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+{
+  "action_dim": 14,
+  "auto_map": {
+    "AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
+  },
+  "min_token": -55,
+  "processor_class": "UniversalActionProcessor",
+  "scale": 10.0,
+  "time_horizon": 30,
+  "vocab_size": 1024
+}

assets/roboeval/fast_tokenizer/special_tokens_map.json

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+{}

assets/roboeval/fast_tokenizer/tokenizer.json

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+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [],
+  "normalizer": null,
+  "pre_tokenizer": {
+    "type": "ByteLevel",
+    "add_prefix_space": false,
+    "trim_offsets": true,
+    "use_regex": true
+  },
+  "post_processor": {
+    "type": "ByteLevel",
+    "add_prefix_space": true,
+    "trim_offsets": false,
+    "use_regex": true
+  },
+  "decoder": {
+    "type": "ByteLevel",
+    "add_prefix_space": true,
+    "trim_offsets": true,
+    "use_regex": true
+  },
+  "model": {
+    "type": "BPE",
+    "dropout": null,
+    "unk_token": null,
+    "continuing_subword_prefix": null,
+    "end_of_word_suffix": null,
+    "fuse_unk": false,
+    "byte_fallback": false,
+    "ignore_merges": false,
+    "vocab": {
+      "\u0000": 0,
+      "\u0001": 1,
+      "\u0002": 2,
+      "\u0003": 3,
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assets/roboeval/fast_tokenizer/tokenizer_config.json

@@ -0,0 +1,11 @@
+{
+  "added_tokens_decoder": {},
+  "auto_map": {
+    "AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
+  },
+  "clean_up_tokenization_spaces": false,
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "processor_class": "UniversalActionProcessor",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

params/_CHECKPOINT_METADATA

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+{"item_handlers": "orbax.checkpoint._src.handlers.pytree_checkpoint_handler.PyTreeCheckpointHandler", "metrics": {}, "performance_metrics": {}, "init_timestamp_nsecs": 1793533220449832653, "commit_timestamp_nsecs": 1793533262408834105, "custom_metadata": {}}

params/_METADATA

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+}