diff --git a/environments/hack0/action_tokenizer.py b/environments/hack0/action_tokenizer.py
new file mode 100644
index 00000000..e589ea1b
--- /dev/null
+++ b/environments/hack0/action_tokenizer.py
@@ -0,0 +1,72 @@
+"""
+action_tokenizer.py
+
+Extension class; wraps base LLM/VLM tokenizer with logic to discretize and tokenize continuous robot actions.
+"""
+
+from typing import List, Union
+
+import numpy as np
+from transformers import PreTrainedTokenizerBase
+
+
+class ActionTokenizer:
+    def __init__(
+        self, tokenizer: PreTrainedTokenizerBase, bins: int = 256, min_action: int = -1, max_action: int = 1
+    ) -> None:
+        """
+        Discretizes continuous robot actions into N bins per dimension and maps to the least used tokens.
+
+        NOTE =>> by default, assumes a BPE-style tokenizer akin to the LlamaTokenizer, where *the least used tokens*
+                 appear at the end of the vocabulary!
+
+        :param tokenizer: Base LLM/VLM tokenizer to extend.
+        :param bins: Number of bins for each continuous value; we'll adopt a uniform binning strategy.
+        :param min_action: Minimum action value (for clipping, setting lower bound on bin interval).
+        :param max_action: Maximum action value (for clipping, setting upper bound on bin interval).
+        """
+        self.tokenizer, self.n_bins, self.min_action, self.max_action = tokenizer, bins, min_action, max_action
+
+        # Create Uniform Bins + Compute Bin Centers
+        self.bins = np.linspace(min_action, max_action, self.n_bins)
+        self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
+
+        # [Contract] Set "action_token_begin_idx" based on `self.tokenizer.vocab_size - (self.n_bins + 1)`
+        #   =>> Assumes we're always overwriting the final `n_bins` tokens of the vocabulary!
+        self.action_token_begin_idx: int = int(self.tokenizer.vocab_size - (self.n_bins + 1))
+
+    def __call__(self, action: np.ndarray) -> Union[str, List[str]]:
+        """Clip & bin actions to *the last `n_bins` tokens* of the vocabulary (e.g., tokenizer.vocab[-256:])."""
+        action = np.clip(action, a_min=float(self.min_action), a_max=float(self.max_action))
+        discretized_action = np.digitize(action, self.bins)
+
+        # Handle single element vs. batch
+        if len(discretized_action.shape) == 1:
+            return self.tokenizer.decode(list(self.tokenizer.vocab_size - discretized_action))
+        else:
+            return self.tokenizer.batch_decode((self.tokenizer.vocab_size - discretized_action).tolist())
+
+    def decode_token_ids_to_actions(self, action_token_ids: np.ndarray) -> np.ndarray:
+        """
+        Returns continuous actions for discrete action token IDs.
+
+        NOTE =>> Because of the way the actions are discretized w.r.t. the bins (and not the bin centers), the
+                 digitization returns bin indices between [1, # bins], inclusive, when there are actually only
+                 (# bins - 1) bin intervals.
+
+                 Therefore, if the digitization returns the last possible index, we map this to the last bin interval.
+
+        EXAMPLE =>> Let's say self._bins has 256 values. Then self._bin_centers has 255 values. Digitization returns
+                    indices between [1, 256]. We subtract 1 from all indices so that they are between [0, 255]. There
+                    is still one index (i==255) that would cause an out-of-bounds error if used to index into
+                    self._bin_centers. Therefore, if i==255, we subtract 1 from it so that it just becomes the index of
+                    the last bin center. We implement this simply via clipping between [0, 255 - 1].
+        """
+        discretized_actions = self.tokenizer.vocab_size - action_token_ids
+        discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
+
+        return self.bin_centers[discretized_actions]
+
+    @property
+    def vocab_size(self) -> int:
+        return self.n_bins
\ No newline at end of file
diff --git a/environments/hack0/open_robot_env.py b/environments/hack0/open_robot_env.py
new file mode 100644
index 00000000..55b808bb
--- /dev/null
+++ b/environments/hack0/open_robot_env.py
@@ -0,0 +1,237 @@
+import random
+from typing import Dict, List, Optional, Tuple, TypedDict, Union
+
+from tqdm.asyncio import tqdm_asyncio
+import robosuite as suite
+from robosuite.controllers import load_part_controller_config
+from transformers import AutoModelForVision2Seq, AutoProcessor
+import torch
+from PIL import Image
+
+from atroposlib.envs.base import (
+    APIServerConfig,
+    BaseEnv,
+    BaseEnvConfig,
+    ScoredDataGroup,
+)
+from atroposlib.type_definitions import Item, number
+from atroposlib.utils.tokenize_for_trainer import tokenize_for_trainer
+from action_tokenizer import ActionTokenizer
+
+prompt = "In: What action should the robot take to pick up the cube?\nOut:"
+
+class RobotSimEnv(BaseEnv):
+
+    name = "robot_sim"
+
+    def __init__(
+        self,
+        config: BaseEnvConfig,
+        server_configs: List[APIServerConfig],
+        slurm=True,
+        testing=False,
+    ):
+        super().__init__(config, server_configs, slurm, testing)
+        self.percent_correct_buffer = list()
+        self.eval_metrics = list()
+        # Add tracking for wandb visualizations
+        self.rollouts_for_wandb = []
+        self.completion_lengths = []
+
+    #! Many methods here will have to be removed 
+    @classmethod
+    def config_init(cls) -> Tuple[BaseEnvConfig, List[APIServerConfig]]:
+
+        #! vllm support for VLA models is a bit iffy. Therefore we don't use vllm. Ideally no model would be used below, but empty/None values throw error therefore placeholder tokenizer_name and model_name is passed. These are not needed for the VLA to run.
+        env_config = BaseEnvConfig(
+            tokenizer_name="NousResearch/DeepHermes-3-Llama-3-3B-Preview",
+            group_size=8,
+            use_wandb=True,
+            rollout_server_url="http://localhost:8000",
+            total_steps=1000,
+            batch_size=12,
+            steps_per_eval=100,
+            max_token_length=2048,
+            wandb_name="gsm8k",
+        )
+        server_configs = [
+            APIServerConfig(
+                model_name="NousResearch/DeepHermes-3-Llama-3-3B-Preview",
+                base_url="http://localhost:9001/v1",
+                api_key="x",
+                num_requests_for_eval=256,
+            ),
+        ]
+
+        return env_config, server_configs
+
+    #! Not sure what to do with this
+    async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
+        if wandb_metrics is None:
+            wandb_metrics = {}
+
+        # Try to calculate percent_correct, pass if there's a division by zero
+        try:
+            wandb_metrics["train/percent_correct"] = sum(
+                self.percent_correct_buffer
+            ) / len(self.percent_correct_buffer)
+        except ZeroDivisionError:
+            # Skip if buffer is empty
+            pass
+
+        self.percent_correct_buffer = list()
+        for item in self.eval_metrics:
+            wandb_metrics[item[0]] = item[1]
+        self.eval_metrics = list()
+        # Call the parent method to handle the server metrics
+        await super().wandb_log(wandb_metrics)
+
+    #! There is NO test data for this environment
+    async def setup(self):
+
+        self.local_model_path = "openvla/openvla-7b"
+
+        self.robosuite_env = robosuite_env = suite.make(
+            "Lift", #This can be changed to any other envirionment like NutAssemblySquare
+            robots="Panda",
+            has_renderer=False,
+            has_offscreen_renderer=True,
+            use_camera_obs=True,
+            camera_names="frontview",
+            camera_heights=640,
+            camera_widths=480
+        )
+
+        self.processor = AutoProcessor.from_pretrained(self.local_model_path, trust_remote_code=True)
+
+        self.vla = AutoModelForVision2Seq.from_pretrained(
+            self.local_model_path,
+            # attn_implementation="flash_attention_2", #Removed for now. #TODO: Add this
+            torch_dtype=torch.bfloat16,
+            low_cpu_mem_usage=True,
+            trust_remote_code=True
+        ).to("cuda:0")
+
+        self.action_tokenizer = ActionTokenizer(self.processor.tokenizer, bins=256, min_action=-1, max_action=1)
+
+        self.max_steps = 100
+        
+        self.iter = 0
+
+    def save_checkpoint(self, step, data=None):
+        if data is None:
+            data = {}
+        data["iter"] = self.iter
+        super().save_checkpoint(step, data)
+
+    #! See where this is called from and then change. No eval data so commenting out for now.
+    async def rollout_and_score_eval(self, question: str, answer: str) -> number:
+        
+        pass
+
+    async def evaluate(self, *args, **kwargs):
+        eval_tasks = []
+        # for item in self.test:
+        #     eval_tasks.append(
+        #         self.rollout_and_score_eval(item["question"], item["gold_answer"])
+        #     )
+        # scores = await tqdm_asyncio.gather(*eval_tasks)
+        # self.eval_metrics.append(("eval/percent_correct", sum(scores) / len(scores)))
+        return None
+
+    async def collect_trajectories(
+        self, item: bool
+    ) -> Tuple[ScoredDataGroup, list[Item]]:
+        
+        obs = self.robosuite_env.reset()
+
+        to_score = list()
+        to_backlog = list()
+
+        print("TRYING TO COLLECT TRAJECTORIES")
+
+        print(self.max_steps)
+
+        for i in range(self.max_steps):
+
+            
+
+            image = Image.fromarray(obs['frontview_image'])
+    
+            inputs = self.processor(prompt, image).to("cuda:0", dtype=torch.bfloat16)
+            action = self.vla.predict_action(**inputs, unnorm_key="bridge_orig", do_sample=False)
+            
+            #! Remove adjustment for now
+            action[0:3] = action[0:3]*100 #because the sensitivity is 0.01
+            action[..., 2] = action[..., 2] * -1.0
+            # action[..., -1] = np.sign(action[..., -1])
+
+            orig_low, orig_high = 0.0, 1.0
+            action[..., -1] = 2 * (action[..., -1] - orig_low) / (orig_high - orig_low) - 1
+            # action[..., -1] = np.sign(action[..., -1])
+            # action[6] = action[6]*2-1 #related to the gripper openvla is [0,1],robosuite is [-1,1]
+            action[..., -1] = action[..., -1] * -1.0
+
+            obs, reward, done, info = self.robosuite_env.step(action)
+
+            print(action)
+
+            to_score.append({"action:": action, "reward": reward})
+
+        # user_message = {"role": "user", "content": item["question"]}
+        # gold_answer = (
+        #     "\\boxed{" + item["answer"].split("#")[-1].strip().replace(",", "") + "}"
+        # )
+
+        # chat_completions = await self.server.chat_completion(
+        #     messages=[{"role": "system", "content": system_prompt}, user_message],
+        #     n=self.config.group_size,
+        #     max_tokens=self.config.max_token_length,
+        # )
+        
+        # for i, chat_completion in enumerate(chat_completions.choices):
+        #     messages = (
+        #         {"role": "system", "content": system_prompt},
+        #         user_message,
+        #         {"role": "assistant", "content": chat_completion.message.content},
+        #     )
+        #     to_score.append(
+        #         {
+        #             "messages": messages,
+        #             "gold_answer": gold_answer,
+        #             "finish_reason": chat_completion.finish_reason,
+        #         }
+        #     )
+
+        
+        to_postprocess = await self.score(to_score)
+        return to_postprocess, to_backlog
+
+    async def score(
+        self, rollout_group_data
+    ) -> Union[Optional[ScoredDataGroup], List[Optional[ScoredDataGroup]]]:
+        scores = ScoredDataGroup()
+        scores["tokens"] = list()
+        scores["masks"] = list()
+        scores["scores"] = list()
+
+        for item in rollout_group_data:
+            action_tokens, mask = self.action_tokenizer.tokenize(item["action"])
+            scores["tokens"].append(action_tokens)
+            scores["masks"].append(mask)
+            scores["scores"].append(item["reward"])
+        return scores
+        
+
+    #! What should this return? A full trajectory of the robot consiting of N steps
+    async def get_next_item(self) -> bool:
+        #! There is no next item, only a reset (or a random seed). See how we can take a random seed. For now just reset.
+
+        #! Another limitation is that right now it has to be 1 by 1. It can't do multiple of this. It has to wait for one to finish. 
+        
+        self.iter += 1
+        return True
+
+
+if __name__ == "__main__":
+    RobotSimEnv.cli()