Normal view

Received yesterday — 31 January 2026

German robot vendor RobCo lands $100 million to expand in U.S.

30 January 2026 at 20:06



The German industrial robotics vendor RobCo has raised $100 million in backing, saying the funds will help to advance its Physical AI roadmap, expand enterprise deployments, and deepen the company’s presence in the U.S. market.

The firm first expanded into the United States in 2025 and now operates in San Francisco and Austin. RobCo says the U.S. has become a major growth market as manufacturers accelerate automation efforts in response to labor constraints, reshoring initiatives, and rising operational complexity.

The “series C” venture capital round was co-led by Lightspeed Venture Partners and Lingotto Innovation, alongside Sequoia Capital, Greenfield Partners, Kindred Capital, Leitmotif, and The Friedkin Group.

Founded in 2020 in Munich, RobCo says it enables increasingly autonomous robot operations inside real production environments including manufacturing and logistics. By combining perception, motion planning, and self-learning methods, RobCo’s platform is designed to reduce friction between today’s processes and end-to-end automation. According to the company, its technology integrates hardware and software as a single full-stack platform. That means its robots can acquire task-specific skills through demonstration and self-learning rather than manual programming, enabling faster deployment, rapid iteration, and easier adaptation to complex or variable processes.

“With $100 million of additional funding, we will become the dominant AI robotics company for manufacturing in the U.S. and Europe” said Roman Hölzl, CEO and Founder of RobCo. “This will allow us to execute on our purpose of automating the ordinary, so humans can do the extraordinary.”

Robots: Still making strides in logistics

30 January 2026 at 14:00



Makers of humanoid robots are targeting logistics, specifically the warehouse, as they continue a steady march to integrate their human-looking machines into today’s increasingly automated workplaces. That’s because research shows that the labor-intensive warehouse is a promising market for the still-nascent technology, which mimics the human body and can perform a range of material handling and order fulfillment tasks.


U.K.-based research firm IDTechEx projects logistics and warehousing will be the second-largest adopter of humanoid robots over the next 10 years, following just behind the automotive industry (see Exhibit 1). Key benefits in the warehouse include bringing precision and consistency to repetitive tasks and improving speed while minimizing human error, the company said in an October market outlook report.

“Facing acute labor shortages and rising operational complexity, warehouses are turning to humanoids as a promising solution,” according to the report. “The benefits are multifaceted: Humanoid robots help lower labor costs, reduce operational disruptions, and offer unmatched flexibility, capable of adapting to varying tasks throughout the day.”

But the research also tells a deeper story: As of last year, humanoid robot deployment in warehouses remained below 5%, due to both technological and commercial roadblocks. Short operating time and long recharge cycles can create substantial downtime, for instance, while limited field testing and safety concerns have left many end-users cautious. A separate industry study, by U.K. researcher Interact Analysis, predicts humanoid robot growth will be relatively slow in the short term, reaching about 40,000 shipments globally by 2032.

“The humanoid robot market is currently experiencing substantial hype, fueled by a large addressable market and significant investment activity,” Rueben Scriven, research manager at Interact Analysis, wrote in the 2025 report. “However, despite the potential, our outlook remains cautious due to several key barriers that hinder widespread adoption, including high prices and the gap in the dexterity needed to match human productivity levels, both of which are likely to persist into the next decade. However, we maintain that there’s a significant potential in the mid- to long term.”

Challenges aside, the work to develop and deploy humanoids continues, with many companies hitting major milestones in 2025 and early 2026. Here’s a look at some of the most recent accomplishments.

DIGIT GETS BUSY

Humanoid robots resemble the human body—in general, they have a torso, head, and two arms and legs, but they can also replicate just portions of the body. Robotic arms can be considered humanoid, as can bots that feature an upper body on a wheeled base. The bipedal variety—those that can walk on two legs—are gaining momentum.

Agility Robotics announced late last year that its bipedal humanoid robot, called Digit, had moved more than 100,000 totes in a commercial environment—at a GXO Logistics facility in Flowery Branch, Georgia. Just a few weeks later, the company said it would deploy Digit robots in San Antonio, Texas, to handle fulfillment operations for e-commerce fulfillment platform Mercado Libre. The companies said they plan to explore additional uses for Digit across Mercado Libre’s warehouses in Latin America. They did not give a timeframe for the rollout.

Agility’s humanoid robots are also in use at facilities run by Amazon and German motion technology company Schaeffler.

Agility is a business unit of Humanoid Global Holdings, which includes robotic companies Cartwheel Robotics, RideScan Ltd., and Formic Technologies Inc. in its portfolio of businesses.

ALPHA BIPEDAL TAKES OFF

U.K.-based robotics and AI (artificial intelligence) developer Humanoid launched its first bipedal robot this past December, introducing HMND 01 Alpha Bipedal. The robot went from design to working prototype in just five months and was up and walking just 48 hours after final assembly—a feat that typically takes weeks or even months, according to the bot’s developers.

Alpha Bipedal stands five feet, 10 inches tall and can carry loads of 33 pounds in its arms. Still in testing, the bot is designed to tackle industrial, household, and service tasks.

“HMND 01 is designed to address real-world challenges across industrial and home environments,” Artem Sokolov, founder and CEO of Humanoid, said in a December statement announcing the launch. “With manufacturing sectors facing labor shortages of up to 27%, leaving significant gaps in production, and millions of people performing physically demanding or repetitive tasks, robots can provide meaningful support. In domestic environments, they have the potential to assist elderly people or those with physical limitations, helping with object handling, coordination, and daily activities. Every day, over 16 billion hours are spent on unpaid domestic and care work worldwide—work that, if valued economically, would exceed 40% of GDP in some countries. By taking on these responsibilities, humanoid robots can free humans to focus on higher-value and safer work, improving their productivity and quality of life.”

HMND 01 Alpha Bipedal follows the September launch of Humanoid’s wheeled Alpha platform, which has been tested commercially and helped extend the company’s reach from industrial and logistics tasks—including warehouse automation, picking, and palletizing—to domestic support applications.

AGILE ONE TAKES OFF

Robotic automation company Agile Robots launched its first humanoid robot, called Agile One, in November. The robot is designed to work in industrial settings, where company leaders say it can operate safely and efficiently alongside humans and other robotic solutions. The bot’s key tasks include material gathering and transport, pick-and-place operations, machine tending, tool use, and fine manipulation.

Agile One will be manufactured at the company’s facilities in Germany.

“At Agile Robots, we believe the next industrial revolution is Physical AI: intelligent, autonomous, and flexible robots that can perceive, understand, and act in the physical world,” Agile Robots’ CEO and founder, Dr. Zhaopeng Chen, said in a statement announcing the launch. “Agile One embodies this revolution.”

The new humanoid is part of the company’s wider portfolio of AI-driven robotic systems, which includes robotic hands and arms as well as autonomous mobile robots (AMRs) and automated guided vehicles (AGVs). All are driven by the company’s AI software platform, AgileCore, and are designed to work together.

“The real value for our industrial customers isn’t just a stand-alone intelligent humanoid, but an entire intelligent production system,” Chen said in the statement. “We see [Agile One] working seamlessly alongside our other robotic solutions, each part of the system, connected and learning from each other. This approach of applying Physical AI to whole production systems can give our customers a new level of holistic efficiency and quality.”

Full production of Agile One begins this year.

​Safety first: Industry updates standards for humanoid robots


As two-legged and four-legged robots begin to find applications in supply chain operations, the sector is refining its safety standards to ensure that humanoid and collaborative robots can be deployed at scale, according to a December report from Interact Analysis.

The work is necessary because the unique mechanics associated with legged robotics introduce new challenges around stability, fall dynamics, and unpredictable motion, according to report author Clara Sipes, a market analyst at Interact Analysis. To be precise, unlike statically stable machines, dynamically stable machines such as humanoids collapse when power is cut, creating residual risk in the event of a fall.

In response, new standards such as the International Organization for Standardization’s ISO 26058-1 and ISO 25785-1 have been developed to address both statically and dynamically stable mobile robotics. In addition, ISO TR (Technical Report) R15.108 examines the challenges associated with bipedal, quadrupedal, and wheeled balancing mobile robots.

According to the Interact Analysis report, one of the most notable shifts is the removal of references to “collaborative modes.” In the most recent revisions, collaborative robots must be evaluated based on the application, not the robot alone, since each application carries its own risks, and the standard now encourages assessing the entire environment within which the robot operates.

Additional changes cover requirements for improved cyber resilience, the report said. European regulatory changes, particularly the Cyber Resilience Act (CRA), AI Act, and Machinery Regulation, are establishing a unified framework for safety, cybersecurity, and risk management. That will shape the future of industrial automation by addressing new vulnerabilities within products that are increasingly connected to a network.

In its report, Interact Analysis advised manufacturers and integrators in the robotic sector to prepare early for the upcoming standards revisions. With multiple regulations taking effect over the next few years, organizations that begin aligning now will avoid costly redesigns and rushed compliance efforts later, the report noted.

—Ben Ames, Senior News Editor

Received before yesterday

Building Generalist Humanoid Capabilities with NVIDIA Isaac GR00T N1.6 Using a Sim-to-Real Workflow 

8 January 2026 at 17:38
A robot thrTo make humanoid robots useful, they need cognition and loco-manipulation that span perception, planning, and whole-body control in dynamic environments. ...A robot thr

To make humanoid robots useful, they need cognition and loco-manipulation that span perception, planning, and whole-body control in dynamic environments. Building these generalist robots requires a workflow that unifies simulation, control, and learning for robots to acquire complex skills before transferring into the real world. In this post, we present NVIDIA Isaac GR00T N1.6…

Source

Accelerating LLM and VLM Inference for Automotive and Robotics with NVIDIA TensorRT Edge-LLM

8 January 2026 at 17:28
Large language models (LLMs) and multimodal reasoning systems are rapidly expanding beyond the data center. Automotive and robotics developers increasingly want...

Large language models (LLMs) and multimodal reasoning systems are rapidly expanding beyond the data center. Automotive and robotics developers increasingly want to run conversational AI agents, multimodal perception, and high-level planning directly on the vehicle or robot – where latency, reliability, and the ability to operate offline matter most. While many existing LLM and vision language…

Source

Build and Orchestrate End-to-End SDG Workflows with NVIDIA Isaac Sim and NVIDIA OSMO 

7 January 2026 at 18:00
As robots take on increasingly dynamic mobility tasks, developers need physics-accurate simulations that translate across environments and workloads. Training...

As robots take on increasingly dynamic mobility tasks, developers need physics-accurate simulations that translate across environments and workloads. Training robot policies and models to do these tasks requires a large amount of diverse, high-quality data, which is often expensive and time-consuming to collect in the physical world. Therefore, generating synthetic data at scale using cloud…

Source

Simplify Generalist Robot Policy Evaluation in Simulation with NVIDIA Isaac Lab-Arena

5 January 2026 at 22:14
Generalist robot policies must operate across diverse tasks, embodiments, and environments, requiring scalable, repeatable simulation-based evaluation. Setting...

Generalist robot policies must operate across diverse tasks, embodiments, and environments, requiring scalable, repeatable simulation-based evaluation. Setting up large-scale policy evaluations is tedious and manual. Without a systematic approach, developers need to build high-overhead custom infrastructure, yet task libraries remain limited in complexity and diversity.

Source

Accelerate AI Inference for Edge and Robotics with NVIDIA Jetson T4000 and NVIDIA JetPack 7.1

5 January 2026 at 22:10
NVIDIA Jetson T4000.NVIDIA is introducing the NVIDIA Jetson T4000, bringing high-performance AI and real-time reasoning to a wider range of robotics and edge AI applications....NVIDIA Jetson T4000.

NVIDIA is introducing the NVIDIA Jetson T4000, bringing high-performance AI and real-time reasoning to a wider range of robotics and edge AI applications. Optimized for tighter power and thermal envelopes, T4000 delivers up to 1200 FP4 TFLOPs of AI compute and 64 GB of memory, providing an ideal balance of performance, efficiency, and scalability. With its energy-efficient design and production…

Source

Building Autonomous Vehicles That Reason with NVIDIA Alpamayo

5 January 2026 at 21:49
Autonomous vehicle (AV) research is undergoing a rapid shift. The field is being reshaped by the emergence of reasoning-based vision–language–action (VLA)...

Autonomous vehicle (AV) research is undergoing a rapid shift. The field is being reshaped by the emergence of reasoning-based vision–language–action (VLA) models that bring human-like thinking to AV decision-making. These models can be viewed as implicit world models operating in a semantic space, allowing AVs to solve complex problems step-by-step and to generate reasoning traces that mirror…

Source

💾

AI Factories, Physical AI, and Advances in Models, Agents, and Infrastructure That Shaped 2025

31 December 2025 at 17:30
Four-image grid illustrating AI agents, robotics, data center infrastructure, and simulated environments.2025 was another milestone year for developers and researchers working with NVIDIA technologies. Progress in data center power and compute design, AI...Four-image grid illustrating AI agents, robotics, data center infrastructure, and simulated environments.

2025 was another milestone year for developers and researchers working with NVIDIA technologies. Progress in data center power and compute design, AI infrastructure, model optimization, open models, AI agents, and physical AI redefined how intelligent systems are trained, deployed, and moved into the real world. These posts highlight the innovations that resonated most with our readers.

Source

Simulate Robotic Environments Faster with NVIDIA Isaac Sim and World Labs Marble

17 December 2025 at 17:00
Building realistic 3D environments for robotics simulation has traditionally been a labor-intensive process, often requiring weeks of manual modeling and setup....

Building realistic 3D environments for robotics simulation has traditionally been a labor-intensive process, often requiring weeks of manual modeling and setup. Now, with generative world models, you can go from a text prompt to a photorealistic, simulation-ready world in a fraction of time. By combining NVIDIA Isaac Sim, an open source robotics reference framework, with generative models such as…

Source

R²D²: Improving Robot Manipulation with Simulation and Language Models

12 December 2025 at 17:00
Robot manipulation systems struggle with changing objects, lighting, and contact dynamics when they move into dynamic real-world environments. On top of this,...

Robot manipulation systems struggle with changing objects, lighting, and contact dynamics when they move into dynamic real-world environments. On top of this, gaps between simulation and reality, and non-optimized grippers or tools often limit how reliably robots can generalize, execute long-horizon tasks, and achieve human-level dexterity across diverse tasks. This edition of NVIDIA Robotics…

Source

Getting Started with Edge AI on NVIDIA Jetson: LLMs, VLMs, and Foundation Models for Robotics

11 December 2025 at 16:00
Running advanced AI and computer vision workloads on small, power-efficient devices at the edge is a growing challenge. Robots, smart cameras, and autonomous...

Running advanced AI and computer vision workloads on small, power-efficient devices at the edge is a growing challenge. Robots, smart cameras, and autonomous machines need real-time intelligence to see, understand, and react without depending on the cloud. The NVIDIA Jetson platform meets this need with compact, GPU-accelerated modules and developer kits purpose-built for edge AI and robotics.

Source

How to Scale Data Generation for Physical AI with the NVIDIA Cosmos Cookbook

1 December 2025 at 17:00
Building powerful physical AI models requires diverse, controllable, and physically-grounded data at scale. Collecting large-scale, diverse real-world datasets...

Building powerful physical AI models requires diverse, controllable, and physically-grounded data at scale. Collecting large-scale, diverse real-world datasets for training can be expensive, time-intensive, and dangerous. NVIDIA Cosmos open world foundation models (WFMs) address these challenges by enabling scalable, high-fidelity synthetic data generation for physical AI and the augmentation of…

Source

R²D²: Perception-Guided Task & Motion Planning for Long-Horizon Manipulation

4 November 2025 at 17:00
Traditional task and motion planning (TAMP) systems for robot manipulation use cases operate on static models that often fail in new environments. Integrating...

Traditional task and motion planning (TAMP) systems for robot manipulation use cases operate on static models that often fail in new environments. Integrating perception with manipulation is a solution to this challenge, enabling robots to update plans mid-execution and adapt to dynamic scenarios. In this edition of the NVIDIA Robotics Research and Development Digest (R²D²)…

Source

Warehouse automation startup gains $120 million new tech

16 January 2026 at 19:13



Supply chain tech provider Mytra has raised $120 million in funding for its industrial robotics and software-defined automation platforms, adding to previous strategic backing from Lineage and RyderVentures, the corporate venture capital arm of Ryder System, Inc.

The “series C” round was led by Avenir Growth. New investors Kivu Ventures, Liquid 2, D. E. Shaw, and Offline Ventures also joined the round, alongside existing investors Eclipse, Greenoaks, Abstract Ventures, and Promus Ventures.

According to California-based Mytra, its technology is needed because material handling and movement represent nearly 50% of manufacturing labor, yet look fundamentally the same as they did a century ago. And that has resulted in more than 400,000 open industrial roles today, heading toward 2 million by 2030, with turnover rates of 50-200%.

In addition, roughly 60% of warehouse footprint is dead space — aisles and clearance that add cost but no value. And approximately 80% of industrial facilities have zero automation because of cost, complexity, and limited flexibility once installed. But Mytra says it abstracts material flow into software-defined primitives — move, store, pick, route — that standardize operations and make every cubic foot of space addressable.

"Most warehouses and industrial facilities can't access the benefits of automation because legacy systems are too costly and inflexible," said Jamie Reynolds, Co-Founder at Avenir Growth. "We believe Mytra represents a fundamental reimagining: a universal system for material flow that breaks free from legacy constraints.”

Siemens completes pilot test of humanoid robot

15 January 2026 at 22:20



The German technology provider Siemens today said it has completed a proof of concept (POC) program demonstrating the use of humanoid robots in industrial logistics, using bots from UK-based tech firm Humanoid.

Humanoid’s HMND 01 wheeled Alpha robot was deployed in real operations at a Siemens facility, marking the first step in a broader partnership between the two companies to test and validate how humanoid robots can be used in real-world environments, the firms said.

The pilot required the robot to perform a tote-to-conveyor destacking task within Siemens’ logistics process, wherein the robot autonomously picked totes from a storage stack, transported them to a conveyor, and placed them at the designated pickup point for human operators.

Specifically, the POC began with a first phase of building a physical twin to support testing, optimization, and rapid iteration. And the second phase involved a two-week on-site deployment at the Siemens Electronics Factory in Erlangen, where partners assessed the robots in a real-world production environment.

According to the firms, the robots met goals including a throughput of 60 tote moves per hour, operation with two different tote sizes, continuous autonomous task execution for more than 30 minutes, and uptime exceeding 8 hours. The project also achieved an overall pick and place success rate and an autonomous pick and place success rate above 90%.

“At Humanoid, we are a commercially driven company. Our focus is on creating robots that deliver measurable value in real-world settings. Working closely with industrial and technology partners allow us to validate our systems against real operational requirements and understand which use cases matter outside the lab,” Artem Sokolov, founder and CEO of Humanoid, said in a release.

❌