Initial commit

.gitattributes

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README.md

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+---
+license: apache-2.0
+library_name: jax
+tags:
+  - robotics
+  - vision-language-action
+  - vla
+  - flow-matching
+  - manipulation
+  - garment-folding
+  - lehome-challenge
+pipeline_tag: robotics
+---
+
+# LeHome Challenge 2026 — Simulation Policy
+
+This is my policy for the **online (simulation) round** of the [LeHome Challenge 2026](https://github.com/lehome-official/lehome-challenge) (ICRA 2026), a competition on bimanual garment folding with a LeRobot SO-ARM101 setup. It placed **1st of 62 teams**.
+
+It is a π0.5-based vision-language-action policy trained with an asynchronous RL + DAgger loop entirely in NVIDIA Isaac Sim.
+
+For how it works and how to run it, please refer to the code repository and the tech report.
+
+- 💻 **Code:** https://github.com/IliaLarchenko/lehome_solution
+- 📄 **Tech report:** _coming soon_
+- ✍️ **Blog post:** https://ilialarchenko.com/projects/lehome2026
+- 🤖 **Real-world final policy:** https://huggingface.co/IliaLarchenko/lehome_real

assets/inference_config.json

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+{
+  "per_garment_type": {
+    "top_long": {
+      "actions_to_execute": 5,
+      "k_execute": 1.0,
+      "actions_to_keep": 6,
+      "execute_in_n_steps": 5,
+      "num_steps": 10,
+      "time_threshold_inpaint": 0.4,
+      "cfg_scale": 7,
+      "noise_temperature": 0.9,
+      "num_rollout_candidates": 2
+    },
+    "top_short": {
+      "actions_to_execute": 5,
+      "k_execute": 1.0,
+      "actions_to_keep": 3,
+      "execute_in_n_steps": 5,
+      "num_steps": 10,
+      "time_threshold_inpaint": 0.4,
+      "cfg_scale": 7,
+      "noise_temperature": 0.7,
+      "num_rollout_candidates": 3
+    },
+    "pant_long": {
+      "actions_to_execute": 3,
+      "k_execute": 1.0,
+      "actions_to_keep": 3,
+      "execute_in_n_steps": 3,
+      "num_steps": 10,
+      "time_threshold_inpaint": 0.5,
+      "cfg_scale": 9,
+      "noise_temperature": 0.7,
+      "num_rollout_candidates": 3
+    },
+    "pant_short": {
+      "actions_to_execute": 3,
+      "k_execute": 1.0,
+      "actions_to_keep": 3,
+      "execute_in_n_steps": 3,
+      "num_steps": 10,
+      "time_threshold_inpaint": 0.5,
+      "cfg_scale": 7,
+      "noise_temperature": 0.7,
+      "num_rollout_candidates": 3
+    }
+  }
+}

assets/sim_rl/fast_tokenizer/metadata.json

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+{
+  "vocab_size": 1024,
+  "scale": 10.0,
+  "encoded_dims": "0:12",
+  "encoded_dim_ranges": [
+    [
+      0,
+      12
+    ]
+  ],
+  "total_encoded_dims": 12,
+  "action_horizon": 30,
+  "num_training_chunks": 3341147,
+  "compression_stats": {
+    "compression_ratio": 8.226127093663598,
+    "mean_token_length": 43.763,
+    "p99_token_length": 80.0,
+    "min_token_length": 7.0,
+    "max_token_length": 100.0
+  }
+}

assets/sim_rl/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/sim_rl/fast_tokenizer/processor_config.json

@@ -0,0 +1,11 @@
+{
+  "action_dim": 12,
+  "auto_map": {
+    "AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
+  },
+  "min_token": -55,
+  "processor_class": "UniversalActionProcessor",
+  "scale": 10.0,
+  "time_horizon": 30,
+  "vocab_size": 1024
+}

assets/sim_rl/fast_tokenizer/special_tokens_map.json

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+{}

assets/sim_rl/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": {
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+      "\u0001": 1,
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assets/sim_rl/fast_tokenizer/tokenizer_config.json

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+{
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+  "processor_class": "UniversalActionProcessor",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

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params/_sharding

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