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In just 60 seconds, discover how Vision‑Language‑Action (VLA) models unify vision, language, and robotic control to power Physical AI in 2026. Using the SigLIP Vision Encoder, Llama‑2 action tokens, and a unified transformer forward pass, VLAs enable robots to interpret camera feeds, understand natural language, and generate precise motor actions. This production guide explains the architecture, real‑world lessons, and why VLAs outperform traditional reinforcement learning in sim‑to‑real transfer.

TL;DR — Quick Insights

• One model, three jobs: A Vision-Language-Action Model (VLA) simultaneously perceives the world through vision, understands the task through language, and outputs physical robot control commands — collapsing three fragmented legacy pipelines into a single end-to-end trainable system.
• The 7B sweet spot: OpenVLA-7B is the practitioner’s reference model in 2026 — open-weights, documented fine-tuning pipeline, and achievable real-time inference on Jetson AGX Orin after AWQ (Activation-aware Weight Quantization) INT4 quantization (54ms per step, 18.5 Hz).
• Data efficiency breakthrough: Cross-robot co-training on the Open X-Embodiment dataset (1M+ trajectories, 22 robot platforms) means fine-tuning to a new task requires only 50–300 real demonstrations — not 50,000.
• Production is harder than research: The three issues that kill lab-to-production transitions — inference latency, safety layer absence, and fine-tuning data quality — are all solvable. This guide covers each one with working code.
• π0 is the architecture to watch: Physical Intelligence’s diffusion-based π0 model produces smoother trajectories than token-prediction VLAs for dexterous tasks. It is not yet open-weights, but its architecture will define the next generation.

The Day the Fragmented Pipeline Finally Failed Us

In 2013, at A*STAR’s Institute for Infocomm Research, my team ran a robotic manipulation experiment: pick up a cylindrical part and place it into a fixture with precise orientation. Using a camera‑based detector, pose estimator, trajectory planner, and PID‑controlled arm, we achieved a 94% success rate in controlled conditions.

But when fluorescent lights were replaced with LED panels by the the facilities team, performance collapsed to 31%. The detector had been trained on fluorescent‑spectrum images, the pose estimator relied on gradients that shifted under LED spectra, and downstream modules amplified the error. No single component failed — the modular architecture itself was wrong.

This cascading failure is exactly what Vision‑Language‑Action (VLA) models are designed to solve. Unlike traditional robotics pipelines with separate perception, planning, and execution modules, VLAs unify them. They learn end‑to‑end relationships between what the robot sees, what it’s instructed, and how it moves — directly from data.

This Production Field Guide (2026) provides practical insights into:

• VLA architecture and design principles
• Training pipelines for robust performance
• Deployment realities in production environments
• Safety patterns that distinguish demos from real‑world systems

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1. What Is a Vision-Language-Action (VLA) Model — Precisely

A VLA model is a neural network that accepts two simultaneous input streams — a visual stream (RGB or RGB-D camera frames) and a language stream (natural language task instruction) — and outputs continuous or discrete low-level robot control commands at every timestep.

The formal mapping:

Input:  {language_instruction: str, rgb_frame: Tensor[H,W,3], state: Optional[Tensor]}
Output: action_t = [Δx, Δy, Δz, Δroll, Δpitch, Δyaw, gripper] ∈ ℝ⁷

This is categorically different from:

• A VLM (Vision-Language Model) like GPT-4V — which outputs text descriptions or answers
• A classical manipulation pipeline — which outputs waypoints through a chain of separate modules
• A pure RL policy — which outputs actions but cannot interpret natural language instructions

1.1 The 3 Key Vision‑Language‑Action (VLA) Production Models of 2026

In 2026, three production‑ready Vision‑Language‑Action (VLA) models are shaping the future of robotics and Physical AI. OpenVLA‑7B from Stanford/UC Berkeley brings open‑weight accessibility, NVIDIA GR00T N1.5 delivers industrial‑grade multimodal control, and π0 (Pi Zero) by Physical Intelligence pioneers diffusion‑based continuous trajectories. Together, they highlight the diverse architectures driving embodied AI forward.

Model	Developer	Architecture	Action Output	Open Weights
OpenVLA-7B	Stanford / UC Berkeley	SigLIP + Llama-2-7B	Discrete tokens (256 bins)	✅ Yes
NVIDIA GR00T N1.5	NVIDIA	Custom multimodal	Continuous (diffusion-assisted)	❌ No
π0 (Pi Zero)	Physical Intelligence	Flow matching diffusion	Continuous trajectories	❌ No

OpenVLA‑7B, developed by Stanford and UC Berkeley, integrates SigLIP with Llama‑2‑7B to deliver discrete token outputs and is the only open‑weight model, making it highly accessible for researchers and startups. NVIDIA’s GR00T N1.5 leverages a custom multimodal architecture with diffusion‑assisted continuous control, optimized for industrial robotics and simulation environments, though its weights remain closed. Pi Zero (π0) by Physical Intelligence introduces flow‑matching diffusion to generate smooth continuous trajectories, pushing the frontier of embodied AI research. Together, these models highlight diverse approaches — open academic collaboration, enterprise‑grade industrial systems, and cutting‑edge diffusion research — that are driving the future of autonomous robotics and AI in 2026.

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2. VLA Architecture Explained: How Vision‑Language‑Action Models Process Inputs in Robotics

Vision‑Language‑Action (VLA) models are at the heart of Physical AI, enabling robots to interpret camera feeds, understand natural language, and generate precise motor actions. In 2026, production‑ready VLAs such as OpenVLA‑7B, NVIDIA GR00T, and Pi Zero showcase how advanced architectures unify perception, planning, and control. Below, we break down the three critical components that make VLAs work.

2.1 The Vision Encoder: SigLIP ViT‑L/16 for Robotic Perception

OpenVLA processes each camera frame using the SigLIP Vision Transformer (ViT‑L/16), pretrained on billions of image‑text pairs. Each 224×224 frame is divided into 16×16 patches, producing 196 tokens. These tokens encode spatial and semantic information, giving robots prior knowledge of objects, materials, spatial relationships, and everyday physics. This web‑scale pretraining allows VLAs to generalize far better than models trained only on limited robot demonstrations — a key advantage for scalable robotics.

2.2 The Action Token Extension: Mapping Language to Robotic Control

The Llama‑2 tokenizer (32,000 base tokens) is extended with 256 special action tokens, each representing a discrete bin of an action dimension. For a 7‑DoF robotic arm, the model generates exactly 7 sequential tokens, translating natural language instructions into precise motor commands. This design bridges human language inputs with low‑level robotic actions, making VLAs uniquely capable of following complex instructions in real time.

The binning is uniform over the normalised action range [-1, 1]:

def continuous_to_action_tokens(action: np.ndarray, n_bins: int = 256) -> list[int]:
    """
    Converts a 7-DoF continuous action vector to discrete bin indices.
    Used during both training (label generation) and inference (decoding).
    
    Args:
        action: np.ndarray of shape (7,) — [Δx, Δy, Δz, Δroll, Δpitch, Δyaw, gripper]
                Values should be pre-normalized to [-1.0, 1.0]
        n_bins: Number of discrete bins per dimension (default: 256)
    
    Returns:
        List of 7 integer token offsets from the action vocabulary start index
    """
    clipped = np.clip(action, -1.0, 1.0)
    # Map [-1, 1] → [0, n_bins-1]
    bin_indices = ((clipped + 1.0) / 2.0 * (n_bins - 1)).round().astype(int)
    return bin_indices.tolist()


def action_tokens_to_continuous(tokens: list[int], n_bins: int = 256) -> np.ndarray:
    """Inverse operation: decode bin indices back to continuous action values."""
    indices = np.array(tokens, dtype=np.float32)
    return (indices / (n_bins - 1)) * 2.0 - 1.0  # Map [0, n_bins-1] → [-1, 1]

2.3 The Unified Transformer Forward Pass: Integrating Vision, Language, and Action

Visual tokens (196) and text tokens (30–60) are concatenated into a single sequence and passed through the 32‑layer Llama‑2‑7B transformer. The model autoregressively predicts outputs: first completing any text, then generating the required action tokens. Importantly, there is no explicit switch between “language mode” and “action mode.” The same next‑token prediction objective applies to both, enabling seamless integration of vision, language, and action. This unified architecture is what makes VLAs powerful generalist models for robotics.

3. Fine-Tuning OpenVLA for Your Robot: A Complete Workflow

Fine-tuning a VLA from the OpenVLA base checkpoint to your specific robot, task, and environment requires four steps. I will cover each with production-grade implementation details.

Step 1 — Environment Setup

# Verified on Ubuntu 22.04, CUDA 12.1, Python 3.10
conda create -n openvla python=3.10 -y
conda activate openvla

# Core dependencies
pip install torch==2.2.0 torchvision==0.17.0 --index-url https://download.pytorch.org/whl/cu121
pip install transformers==4.40.0 accelerate==0.30.0 peft==0.11.0
pip install datasets timm einops

# OpenVLA-specific utilities
pip install git+https://github.com/openvla/openvla.git

# Verify GPU visibility
python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}, GPUs: {torch.cuda.device_count()}')"

Step 2 — Dataset Collection and Formatting

Demonstrations must be recorded at 30 Hz as synchronized (timestamp, rgb_image, robot_state, action) tuples. The critical quality criteria I learned from failed fine-tuning attempts at Moovita:

• Workspace coverage: Each demonstration must cover the full intended workspace variation — not just the easiest 20% of pick locations
• Recovery demonstrations: At least 15% of demonstrations should show recovery from near-failure states (gripper slightly misaligned, object at edge of workspace). Models fine-tuned only on successful demonstrations fail catastrophically at edge cases
• Language instruction diversity: Vary the phrasing of the same task across demonstrations (“pick up the block”, “grasp the cube”, “lift the red piece”) — this prevents the model from learning a text-matching shortcut instead of genuine instruction following

from dataclasses import dataclass
from pathlib import Path
import numpy as np
from PIL import Image
import json

@dataclass
class DemonstrationStep:
    timestamp_ns: int
    rgb_image: np.ndarray      # shape (H, W, 3), uint8
    language_instruction: str
    robot_state: np.ndarray    # shape (7,) — joint positions or EE pose
    action: np.ndarray         # shape (7,) — [Δx, Δy, Δz, Δroll, Δpitch, Δyaw, gripper]
    is_terminal: bool


def save_demonstration_to_rlds(
    steps: list[DemonstrationStep],
    output_dir: Path,
    episode_id: str
) -> None:
    """
    Saves a demonstration episode in RLDS (Reinforcement Learning Dataset Specification)
    format compatible with OpenVLA's fine-tuning dataloader.
    """
    episode_dir = output_dir / episode_id
    episode_dir.mkdir(parents=True, exist_ok=True)
    
    metadata = {
        "episode_id": episode_id,
        "num_steps": len(steps),
        "language_instruction": steps[0].language_instruction,
        "robot_state_dim": 7,
        "action_dim": 7
    }
    
    for t, step in enumerate(steps):
        # Save image as JPEG (significant size reduction vs PNG for training)
        img_path = episode_dir / f"frame_{t:06d}.jpg"
        Image.fromarray(step.rgb_image).save(img_path, quality=95)
        
        # Save numeric data as compressed numpy archive
        np.savez_compressed(
            episode_dir / f"step_{t:06d}.npz",
            state=step.robot_state,
            action=step.action,
            terminal=np.array([step.is_terminal])
        )
    
    with open(episode_dir / "metadata.json", "w") as f:
        json.dump(metadata, f, indent=2)
    
    print(f"Saved episode {episode_id}: {len(steps)} steps → {episode_dir}")

Step 3 — LoRA Fine-Tuning

import torch
from transformers import AutoModelForCausalLM, AutoProcessor
from peft import LoraConfig, get_peft_model, TaskType
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingLR

# ── Configuration ──────────────────────────────────────────────────────────────
MODEL_ID = "openvla/openvla-7b"
OUTPUT_DIR = "./openvla-finetuned-my-robot"
BATCH_SIZE = 4        # per GPU; use gradient accumulation if < 8
GRAD_ACCUM = 8        # effective batch size = BATCH_SIZE × GRAD_ACCUM × num_GPUs
LEARNING_RATE = 2e-4
NUM_EPOCHS = 3
LORA_RANK = 32        # increase to 64 for complex tasks; decreases fine-tuning speed

# ── Load base model in bfloat16 (training precision) ──────────────────────────
print(f"Loading {MODEL_ID}...")
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    low_cpu_mem_usage=True
)
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)

# ── Configure LoRA — inject into all attention projection layers ───────────────
lora_config = LoraConfig(
    r=LORA_RANK,
    lora_alpha=LORA_RANK * 2,       # Standard rule: alpha = 2 × rank
    target_modules=[
        "q_proj", "k_proj", "v_proj", "o_proj",    # Self-attention
        "gate_proj", "up_proj", "down_proj"          # MLP feed-forward
    ],
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.CAUSAL_LM
)

model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# Expected: trainable params: ~20M || all params: 7.4B || trainable%: ~0.27%

# ── Optimizer with layer-wise learning rate decay ─────────────────────────────
optimizer = AdamW(
    [p for p in model.parameters() if p.requires_grad],
    lr=LEARNING_RATE,
    weight_decay=0.01,
    betas=(0.9, 0.95)
)
scheduler = CosineAnnealingLR(optimizer, T_max=NUM_EPOCHS, eta_min=LEARNING_RATE / 10)

print(f"Fine-tuning configuration:")
print(f"  LoRA rank: {LORA_RANK} | Alpha: {LORA_RANK * 2}")
print(f"  Effective batch size: {BATCH_SIZE * GRAD_ACCUM}")
print(f"  Learning rate: {LEARNING_RATE} → cosine decay to {LEARNING_RATE/10}")

Step 4 — Edge Deployment with TensorRT-LLM

"""
Production inference pipeline for OpenVLA-7B on NVIDIA Jetson AGX Orin.
Achieves 18+ Hz after AWQ INT4 quantization + TensorRT-LLM compilation.

Benchmark results (Jetson AGX Orin, 64GB):
  FP16 native:              ~218ms / step  →  4.6 Hz  ❌ too slow
  AWQ INT4:                  ~89ms / step  → 11.2 Hz  ✅ adequate  
  AWQ INT4 + TensorRT-LLM:   ~54ms / step  → 18.5 Hz  ✅ production-ready
"""

import time
import numpy as np
from dataclasses import dataclass
from typing import Optional
import tensorrt_llm
from PIL import Image

@dataclass
class RobotAction:
    delta_x: float       # metres per control step
    delta_y: float
    delta_z: float
    delta_roll: float    # radians per control step
    delta_pitch: float
    delta_yaw: float
    gripper: float       # 0.0 = fully open, 1.0 = fully closed


class ProductionVLAPipeline:
    """
    Minimal-latency VLA inference pipeline for Jetson edge deployment.
    Handles vision preprocessing, token encoding, TRT inference, and action decoding.
    """
    
    # SigLIP normalization constants (ImageNet-derived, tuned for SigLIP)
    VISION_MEAN = np.array([0.5, 0.5, 0.5], dtype=np.float32)
    VISION_STD  = np.array([0.5, 0.5, 0.5], dtype=np.float32)
    INPUT_RESOLUTION = 224  # pixels (SigLIP ViT-L/16 input size)
    N_ACTION_BINS = 256
    
    def __init__(self, engine_path: str, tokenizer_path: str):
        print(f"Loading TRT engine from {engine_path}...")
        self._engine = tensorrt_llm.runtime.Engine.from_file(engine_path)
        self._ctx = self._engine.create_execution_context()
        self._tokenizer = self._load_tokenizer(tokenizer_path)
        self._warmup()
        print("Pipeline ready.")
    
    def _load_tokenizer(self, path: str):
        from transformers import AutoTokenizer
        tok = AutoTokenizer.from_pretrained(path)
        # Confirm action vocabulary extension is present
        assert len(tok) > 32000, "Action tokens not found in tokenizer vocabulary"
        return tok
    
    def _warmup(self, n_warmup: int = 3):
        """Run N dummy inference steps to warm up TRT execution graph."""
        dummy_frame = np.zeros((224, 224, 3), dtype=np.uint8)
        for _ in range(n_warmup):
            self.step(dummy_frame, "warm up")
    
    def preprocess_frame(self, rgb_frame: np.ndarray) -> np.ndarray:
        """Resize and normalize a raw camera frame for SigLIP input."""
        img = Image.fromarray(rgb_frame).resize(
            (self.INPUT_RESOLUTION, self.INPUT_RESOLUTION),
            resample=Image.BILINEAR
        )
        arr = np.array(img, dtype=np.float32) / 255.0
        arr = (arr - self.VISION_MEAN) / self.VISION_STD
        return np.transpose(arr, (2, 0, 1))  # HWC → CHW
    
    def step(self, rgb_frame: np.ndarray, instruction: str) -> Optional[RobotAction]:
        """Single inference step. Returns None if inference fails safety checks."""
        t_start = time.perf_counter()
        
        # Encode inputs
        vision_tensor = self.preprocess_frame(rgb_frame)
        text_tokens = self._tokenizer.encode(
            f"What action should the robot take to {instruction}?",
            return_tensors="np"
        )
        
        # TRT inference
        try:
            outputs = self._ctx.execute_v2({
                "input_ids": text_tokens.astype(np.int32),
                "pixel_values": vision_tensor.astype(np.float32)
            })
        except RuntimeError as e:
            print(f"[WARNING] TRT inference failed: {e}. Returning None.")
            return None
        
        # Decode last 7 tokens as action
        predicted_ids = outputs["predicted_token_ids"]
        if len(predicted_ids) < 7:
            print("[WARNING] Fewer than 7 action tokens predicted. Returning None.")
            return None
        
        action_token_ids = predicted_ids[-7:]
        
        # Convert from absolute vocabulary indices to bin indices
        action_vocab_start = 32000  # base vocab size
        bin_indices = action_token_ids - action_vocab_start
        
        # Validate bin range before dequantization
        if not np.all((bin_indices >= 0) & (bin_indices < self.N_ACTION_BINS)):
            print(f"[WARNING] Action token out of range: {bin_indices}. Returning None.")
            return None
        
        # Dequantize: [0, 255] → [-1.0, 1.0]
        normalized = (bin_indices.astype(np.float32) / (self.N_ACTION_BINS - 1)) * 2.0 - 1.0
        
        # Denormalize to physical robot limits (example: 5cm max translation per step)
        TRANSLATION_SCALE = 0.05   # metres per step maximum
        ROTATION_SCALE = 0.10      # radians per step maximum
        
        latency_ms = (time.perf_counter() - t_start) * 1000
        
        action = RobotAction(
            delta_x=float(normalized[0] * TRANSLATION_SCALE),
            delta_y=float(normalized[1] * TRANSLATION_SCALE),
            delta_z=float(normalized[2] * TRANSLATION_SCALE),
            delta_roll=float(normalized[3] * ROTATION_SCALE),
            delta_pitch=float(normalized[4] * ROTATION_SCALE),
            delta_yaw=float(normalized[5] * ROTATION_SCALE),
            gripper=float(np.clip(normalized[6], 0.0, 1.0))
        )
        
        print(f"Step: {latency_ms:.1f}ms | {1000/latency_ms:.1f}Hz | "
              f"grip={action.gripper:.2f} | Δz={action.delta_z*100:.1f}cm")
        return action

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4. Lessons Learned from Vision‑Language‑Action (VLA) Deployments: What Research Papers Don’t Tell You

While benchmark leaderboards highlight accuracy and performance, the true lessons of Vision‑Language‑Action (VLA) models come from real deployments in robotics and autonomous systems. These insights reveal failure modes, safety risks, and optimization strategies that are rarely discussed in academic papers but are critical for scaling Physical AI into production.

These are failure modes discovered through real deployments — not from benchmark leaderboards.

Lesson 1 — Bin Resolution Limits Precision in Robotic Assembly

Discrete action bins (256 over a ±5cm range) provide ~0.39mm resolution — sufficient for pick‑and‑place tasks but inadequate for sub‑millimetre assembly, such as connector pins with 0.5mm pitch. For high‑precision tasks, a hybrid architecture is required: use VLAs for high‑level motion, then switch to a fine‑grained continuous regression head near the target. This approach is already used in surgical robotics systems.

Lesson 2 — Safety Wrappers Are Non‑Negotiable in VLA Systems

In Moovita’s early deployments, a software bug caused an arm to move at 10× intended velocity, resulting some hardware damage. The lesson: every VLA output must pass through a deterministic safety filter that enforces velocity limits, workspace boundaries, joint constraints, and continuity checks. VLAs provide suggestions — but the safety layer must have final authority.

Lesson 3 — Data Quality Beats Quantity in VLA Fine‑Tuning

Collecting hundreds of nominal demonstrations without recovery examples leads to brittle models. Robust VLAs require deliberate recovery demonstrations — showing corrective motions when slightly misaligned. 100 recovery demos can outperform 400 nominal ones, proving that quality and diversity of data matter more than sheer volume.

Lesson 4 — VLAs Achieve Superior Sim‑to‑Real Transfer

Unlike reinforcement learning (RL) policies that overfit to simulated visuals, VLAs benefit from internet‑scale pretraining. In internal tests, an OpenVLA fine‑tuned on 200 real demos outperformed a PPO policy trained on 50,000 simulation episodes when evaluated on real robots with novel object placements. VLAs’ domain‑invariant object representations make them far more effective for sim‑to‑real transfer.

Lesson 5 — Temporal Context Matters for Sequential Robotic Tasks

Standard OpenVLA processes frames independently, lacking explicit memory of prior steps. For multi‑step tasks (“open drawer, pick block, close drawer”), the task controller must dynamically encode progress into language instructions. Example: at step 1, “open the drawer”; at step 2, “drawer is open — pick up the block.” Stateful instruction management ensures VLAs handle sequential workflows reliably.

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5. FAQs on Vision‑Language‑Action Models (VLA)

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6. UDHY Learning Path: From Foundations to VLA Deployment

Stage	UDHY Module	Hours	Skills Unlocked
1 — Foundation	Introduction to AI	3–5h	Python, AI concepts
2 — ML Core	Machine Learning Fundamentals	6–8h	PyTorch, gradient descent, supervised learning
3 — Deep Learning	Deep Learning for Robotics	10–14h	Transformers, ViT, TensorRT edge inference
4 — RL Policies	Reinforcement Learning for Robotics	10–14h	PPO, action spaces, reward shaping
5 — VLA Expert	Physical AI & VLA Models	20–30h	End-to-end VLA fine-tuning, edge deployment, safety

Also read: What Is Physical AI? The Complete 2026 Guide — UDHY’s deep-dive on the broader Physical AI landscape.

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7. References

• Kim et al. (2024). OpenVLA: An Open-Source Vision-Language-Action Model. Stanford University / UC Berkeley.
• Brohan et al. (2023). RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. Google DeepMind.
• Open X-Embodiment Collaboration (2024). Open X-Embodiment Dataset and RT-X Models. Google DeepMind GitHub.
• NVIDIA TensorRT Developer Documentation. NVIDIA Developer Network.
• PEFT: Parameter-Efficient Fine-Tuning Library. Hugging Face.
• IEEE Robotics and Automation Society. Research publications and career resources.
• Community discussion: r/MachineLearning — VLA deployment threads. Reddit.
• ROS Discourse — Physical AI community. Open Robotics.

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About the Author

Dr. Dilip Kumar Limbu Co-Founder, Moovita | Former Principal Scientist, A*STAR | PhD, Auckland University of Technology
Connect via LinkedIn Direct Inquiry.

Disclaimer
The views expressed here are personal and based on 30+ years in the industry, including my work at Moovita. They do not necessarily reflect the views of any organization.

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