Origami Robotics
Manipulate Anything Robot
About
We are building a "manipulate anything" model with the robot hardware that embodies it. We designed a hand-based data-collection device and a high DOF, direct drive robotic hand that match each other exactly, enabling us to eliminate embodiment gap and collect real-world data and deploy it directly. We want to scale real world manipulation data by deploying our devices "in the wild" like manufacturing factories, logistic centers to collect Tesla like data, use these data to train our model and then provide automation solutions to these industries.
Founders
AI Research Report
Problem & Solution
Problem and Solution Report: Origami Robotics
Origami Robotics addresses a fundamental bottleneck in the field of robotics: the 'embodiment gap' and the lack of general-purpose manipulation. Most current robotic systems are designed for highly specific, repetitive tasks in controlled environments. When these robots are required to handle diverse objects or operate in 'the wild' (unstructured manufacturing or logistics settings), they often fail because their hardware and software are not unified. This lack of versatility prevents the widespread adoption of robotics for complex, human-like manipulation tasks.
The significance of this problem lies in the massive amount of manual labor still required in logistics and manufacturing for tasks that involve dexterous handling. Traditional robots lack the data and the physical flexibility to 'learn' how to manipulate arbitrary objects, leading to high costs for custom engineering every time a new task is introduced.
Origami Robotics' solution is a 'manipulate anything' model built on a foundation of co-designed hardware and software. They have developed a high-degree-of-freedom (DOF), direct-drive robotic hand alongside a matching hand-based data-collection device. By ensuring the data-collection tool and the actual robotic hand match exactly, they eliminate the embodiment gap. This allows the company to collect large-scale, real-world manipulation data and deploy learned policies directly to the robot without the need for complex translations between different physical forms.
The value proposition of this approach is the creation of reusable 'skills' and replayable robot actions. Instead of programming a robot for one specific task, Origami Robotics enables the training of models that can be reconfigured for various cells and tasks. This infrastructure-first approach aims to solve general manipulation, making robotics as flexible and adaptable as the human workers they are designed to assist.
Market & Competitors
Market and Competitors Report: Origami Robotics
Origami Robotics operates in a highly competitive and rapidly evolving market for industrial automation and physical AI. The market is characterized by a shift away from rigid, single-purpose industrial robots toward flexible, intelligent systems capable of general manipulation. The target audience includes large-scale logistics providers, e-commerce fulfillment centers, and manufacturers looking to automate complex assembly or sorting tasks that previously required human dexterity.
The competitive landscape is divided into two main categories: large incumbents and emerging AI-driven startups. Large incumbents include established players like FANUC, ABB, Yaskawa, and KUKA. These companies dominate the traditional industrial robotics space with high-reliability arms but often lack the specialized, high-DOF 'hand' and the 'manipulate anything' software models that Origami Robotics is developing.
Direct competitors among startups include companies like Flexiv Robotics, Agile Robots, and GrayMatter Robotics. These firms also focus on adaptive robotics and AI-driven manipulation. Additionally, companies like Sanctuary AI and Orangewood Labs are working on humanoid or highly dexterous manipulators that overlap with Origami's mission. Other notable players in the broader automation space include Rise Robotics and Sanctuary AI, which focus on different aspects of robotic power and general intelligence.
Origami Robotics' competitive advantage lies in its integrated approach to hardware and data. By co-designing the data-collection device with the robotic hand, they can generate high-fidelity datasets that are more effective for training manipulation models than generic datasets. Their focus on 'agent chaining' for reconfigurable cells and the creation of reusable skills provides a software-led advantage that allows for faster deployment and greater flexibility than traditional automation solutions. However, as a seed-stage company, their primary disadvantage is the scale of resources and established distribution networks held by the larger incumbents.
Total Addressable Market
Quantitative TAM Report: Origami Robotics
Origami Robotics operates at the intersection of warehouse automation, industrial robotics, and specialized robotic end-effectors. To estimate the Total Addressable Market (TAM), we look at these three overlapping sectors. The global warehouse automation market was valued at approximately $31.21 billion in 2025 and is projected to reach $119.86 billion by 2034, growing at a CAGR of 16.13%. Simultaneously, the industrial robotics market is estimated at $33.96 billion in 2024, with a projected value of $60.56 billion by 2030.
A more specialized segment relevant to Origami Robotics is the robotic gripper and soft gripper market. This niche was valued at $1.36 billion in 2023 and is expected to grow to $3.10 billion by 2032. Because Origami Robotics provides both the high-DOF hardware (the hand) and the software models for general manipulation, their TAM is a composite of the hardware sales in the gripper market and the high-value automation software/RaaS (Robotics as a Service) segments within the broader industrial and warehouse markets.
Using a bottom-up estimation methodology, if Origami Robotics targets the manipulation-heavy tasks that currently represent roughly 10-15% of the warehouse and industrial robotics market, their immediate addressable market is in the range of $3 billion to $5 billion. As their 'manipulate anything' model matures and enables automation in previously unreachable 'in the wild' manufacturing and logistics environments, this addressable segment could expand significantly.
Financially, the company is in its early stages, having raised approximately $500,000 in seed funding as part of the Y Combinator Winter 2026 batch. This initial capital is directed toward data collection and hardware refinement. Given the high CAGR of the target markets (9.9% to 16.1%), the company is positioned in a rapidly expanding financial landscape where the demand for versatile, data-driven manipulation is outstripping the capabilities of traditional, rigid robotic systems.
Founder Analysis
Founders and Background Report: Origami Robotics
Origami Robotics was co-founded by Ryan Xie and Quanting (Daniel) Xie. The founding team is primarily composed of PhDs and engineers originally from the Carnegie Mellon University (CMU) Robotics Institute, a world-renowned center for robotics research. This academic pedigree establishes a strong technical foundation for the company's mission to solve general manipulation in robotics through advanced physical AI.
Ryan Xie serves as a primary founder and brings significant practical experience in the robotics industry. He graduated from the University of Michigan with a degree in Electrical Engineering and Computer Science (EECS), focusing specifically on robotics. Before founding Origami Robotics, Ryan worked as a robotics engineer at Canvas, a startup known for developing drywall-finishing robots. His background also includes involvement with Apochs, further demonstrating his experience in applying robotic solutions to real-world industrial challenges.
Quanting (Daniel) Xie is the other key co-founder, identified as a roboticist with deep ties to the CMU Robotics Institute. His expertise aligns with the company's focus on high-degree-of-freedom (DOF) hardware and complex manipulation models. Together, the founders have positioned Origami Robotics as a participant in the Y Combinator Winter 2026 batch, securing early-stage seed funding to scale their vision of 'manipulate anything' robotic systems.
The leadership team is currently small, reported at approximately five members, but it is highly specialized. By combining CMU’s research-driven approach with Ryan Xie’s experience in industrial robot deployment, the founders aim to bridge the gap between theoretical AI models and practical, hardware-integrated robotic manipulation.
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