Listen to this article

The Robot Report tracked 39 robotics investments that totaled at least $1.14 billion in funding in October 2021. There was one merger and acquisition of two LiDAR companies for an undisclosed amount.

The largest round of the month went to Israel-based Fabric, which raised $200 million. Formerly known as CommonSense Robotics, the company develops AS/RS systems for micro-fulfillment. The Series C funding valued Fabric at more than $1 billion. The company has raised more than $336 million since it was founded in 2015.

The second-largest round went to Dexterity, a U.S.-based developer that raised $140 million to scale its full-stack picking robots for logistics applications. Dexterity founder Samir Menon recently appeared on The Robot Report Podcast to discuss the company’s technology and rapid growth.

Companies developing robots for logistics applications raised the most rounds with seven. Autonomous driving companies raised the second-most rounds at six, followed by sensors (five), industrial robots (three) and aerial drones (three).

Here is a breakdown of the funding types for October 2021, in order of most to least: Seed (11), Series C (8), Venture (7), Series A (5), Other (5), Series B (2), and post-IPO equity (1).

U.S.-based companies accounted for 19 funding rounds in October 2021, while China was second with six rounds. Israel and the U.K. each account for three rounds, while France and Germany each had two. The table below lists robotics company fundings in millions of U.S. dollars, where amounts were publicly available. If more of the funding amounts become available, we will update the chart.

Robotics Investments October 2021

Company	Round	Amount ($M)	Tech	Country
Wootzano	Other	3.4	Logistics robots	U.K.
Techman Robot	Other		Collaborative robots	Taiwan
Virtual Incision	Other	46	Surgical robots	U.S.
Ganymed Robotics	Other	2.8	Surgical robots	France
GrayMatter Robotics	Other	1	Industrial robots	U.S.
Arbe	Post-IPO Equity	100	Sensors	Israel
Gongye Technology	Seed	15.6	Recycling robots	China
Rendered.ai	Seed	6	Synthetic data	U.S.
Poladrone	Seed	4.3	Aerial drones	Malaysia
Ishitva Robotic Systems	Seed	1	Industrial robots	India
Fox Robotics	Seed	1	Logistics robots	U.S.
Aquiline Drones	Seed	1	Aerial drones	U.S.
Leadgen Technology	Seed		Autonomous driving	China
PhiGent Robotics	Seed		Autonomous driving	China
Qinglei Technology	Seed		Sensors	China
Esper Bionics	Seed		Medical robotics	Ukraine
Sensory Robotics	Seed		Sensors	U.S.
fruitcore robotics	Series A	20	Logistics robots	Germany
Humanising Autonomy	Series A	11	Human robot interaction	U.K.
Burro	Series A	10.9	Agrobotics	U.S.
RoboTire	Series A	10.6	Industrial robots	U.S.
BeBop Sensors	Series A	1.4	Sensors	U.S.
Dexterity	Series B	140	Logistics robots	U.S.
MainBot	Series B	14	Educational robots	France
Fabric	Series C	200	Logistics robots	Israel
Hailo	Series C	136	Computing	Israel
Saildrone	Series C	100	Maritime drones	U.S.
Reliable Robotics	Series C	100	Unmanned aerial robots	U.S.
Clarifai	Series C	60	Deep learning	U.S.
JIMU Intelligent	Series C	31.2	Autonomous driving	China
SimScale	Series C	29.2	Simulation software	Germany
Tactile Mobility	Series C	27	Autonomous driving	U.S.
Interlink Electronics	Venture	3	Sensors	U.S.
DroneBase	Venture	20	Aerial drones	U.S.
RightHand Robotics	Venture	19	Logistics robots	U.S.
Wayve	Venture	13.5	Autonomous driving	U.K.
Covariant	Venture	9.5	Logistics robots	U.S.
Cobot Nation	Venture	3.4	Integration	U.S.
UISEE	Venture		Autonomous driving	China

Robotics mergers & acquisitions

The Robot Report tracked only one merger and acquisition in October 2021. Ouster announced it was buying fellow LiDAR maker Sense Photonics. After the acquisition, Sense Photonics will become the “Ouster Automotive” division of Ouster. The new group will be under the direction of Sense Photonics CEO Shauna McIntyre and will focus entirely on solid-state LiDAR sensing products for the automotive market.

Acquirer	Acquired Company	Amount ($M)	Tech	Story
Ouster	Sense Photonic		LiDAR	Story

Editors’ note: What defines robotics investments? The answer to this simple question is central in any attempt to quantify them with some degree of rigor. To make investment analyses consistent, repeatable, and valuable, it is critical to wring out as much subjectivity as possible during the evaluation process. This begins with a definition of terms and a description of assumptions.

Investors and investing
Investment should come from venture capital firms, corporate investment groups, angel investors, and other sources. Friends-and-family investments, government/non-governmental agency grants, and crowd-sourced funding are excluded.

Robotics and intelligent systems companies
Robotics companies must generate or expect to generate revenue from the production of robotics products (that sense, analyze, and act in the physical world), hardware or software subsystems and enabling technologies for robots, or services supporting robotics devices. For this analysis, autonomous vehicles (including technologies that support autonomous driving) and drones are considered robots, while 3D printers, CNC systems, and various types of “hard” automation are not.

Companies that are “robotic” in name only, or use the term “robot” to describe products and services that that do not enable or support devices acting in the physical world, are excluded. For example, this includes “software robots” and robotic process automation. Many firms have multiple locations in different countries. Company locations given in the analysis are based on the publicly listed headquarters in legal documents, press releases, etc.

Verification
Funding information is collected from a number of public and private sources. These include press releases from corporations and investment groups, corporate briefings, industry analysts, and association and industry publications, including Crunchbase PitchBook and Tracxn. In addition, information comes from sessions at conferences and seminars, as well as during private interviews with industry representatives, investors, and others. Unverifiable investments are excluded.