The R&D Tax Aspects of Robot Software

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Robot-Software Responsible for efficiency, speed, precision, and many other desirable features, the software architecture is commonly referred to as the “brains” of robots. Software innovation is simultaneously a cause and a result of the evermore widespread adoption of robotic systems.

On one hand, the robotic industry is responding to the needs of end-users and creating friendlier, easier to integrate, customizable robot software. On the other hand, new capabilities, such as the ability to work alongside humans, have enabled the emergence of unprecedented robotic applications.
The present article will discuss the most recent trends in robot software and present the federal R&D tax credit opportunity available for the robotics software industry.

The Research & Development Tax Credit

Enacted in 1981, the Federal Research and Development (R&D) Tax Credit allows a credit of up to 13 percent of eligible spending for new and improved products and processes. Qualified research must meet the following four criteria:

  • New or improved products, processes, or software
  • Technological in nature
  • Elimination of uncertainty
  • Process of experimentation

Eligible costs include employee wages, cost of supplies, cost of testing, contract research expenses, and costs associated with developing a patent. On January 2, 2013, President Obama signed the bill extending the R&D Tax Credit for 2012 and 2013 tax years.

Software for Robots

Software works as a robot’s brain. Thanks to complex software architectures, industrial robots are capable of performing their tasks quickly, repeatedly, and accurately. Software is also at the heart of robots’ flexibility. The combination of a software baseline with customized built-in architectures has allowed for a myriad of robotic applications. Different applications, though very unique, may have a common base architecture that creates system functionality and enables their seamless performance.
The development of more sophisticated software responds to the evolving needs of end-users and reflects innovative robotics applications across a wide variety of industries.
Three aspects are particularly important when considering robotic software: 1) performance; 2) integration; and 3) user interface. The software architecture, which is composed of the controller, enabling application, and vertical solution levels, must not only enable an efficient and reliable performance but it must also integrate with necessary technologies and be easily deployed by end-users.

Standardization in an Open-Source World

Though many have pushed for the standardization of industrial robots, there has been a surge in demand for application-specific machines.  This means that robots must be able to perform high-accuracy tasks, such as wafer handling, as well as high-rigidity ones, like car body transfers.

The outstanding challenge is to establish enhanced commonality while keeping up with the fast-paced technological advances that often encourage consumers to migrate to new, more up-to-date systems. Among the benefits of standardization are the easier implementation and operation of robotic software. In other words, robots become less complicated.
While proprietary robot software remains dominant, the advent of the Robot Operating System (ROS) has triggered a movement towards open-source architectures. A growing number of robot hardware manufacturers, commercial research labs, and software companies are adopting the ROS platform, which works as a common and flexible framework for writing robot software.
Licensed under an open-source, BDS license, ROS provides libraries, tools, and conventions to enable the creation of new robotic applications. Its ultimate objective is to simplify the development of truly robust, general-purpose robot software that can be used across a variety of robotic platforms.
Created in 2007 by robotics research lab and technology incubator Willow Garage, ROS was initially developed for personal and service robotic applications. A recent partnership between the Southwest Research Institute (SwRI) and Willow Garage, however, extended the advanced capabilities of the ROS platform to manufacturing, creating the new ROS-Industrial.

In addition to drivers, sensors, and communication buses specially designed for industrial manipulators, ROS-I’s capabilities include advanced perception and path/grasp planning that allow for previously technically infeasible or cost-prohibitive industrial robotic applications. The platform also features quality standards and software development practices aimed at ensuring higher reliability.
According to Paul Hvass, Senior Research Engineer with SwRI, ROS-I has worked as a bridge between the academic and the industrial worlds. The innovative open source platform allows end-users to use newly developed code from academic researchers without having to overcome the restrictions of proprietary software. The possibility of leveraging new research on existing industrial platforms promises to speed up robotic software R&D, particularly when it comes to unstructured, collaborative applications. Also, the portability of ROS enables unprecedented collaboration within the research community, allowing different groups to build upon each other’s work and increasing everyone’s chances of success.
By providing a common interface, ROS-I allows end-users to use the same application architecture on different robots with minimal code changes. In other words, it reduces manufacturer “lock-in” by standardizing robot and sensor interfaces across many industrial platforms.

The multiplication of ROS-friendly robots points towards the consolidation of an open-source strategy in the robotics industry. This approach makes it easier for people to experiment and paves the way for the development of new, lower-cost technologies and applications.

The following table summarizes the advantages of ROS Industrial:

  • Custom inverse kinematics for manipulators, including solutions for manipulators with greater than six degrees-of-freedom
  • Advanced 2-D (image) and 3-D (point cloud) perception
  • Rich toolset for development, simulation, and visualization
  • Unstructured applications that include advanced perception for identifying robot work pieces as opposed to hard tooling
  • Completely dynamic path planning based upon advanced perception and models as opposed to simply replaying taught paths
  • Model-based approaches that permit automated programming for thousands of unique CAD parts
  • Eliminates path planning and teaching; collision-free, optimal paths are automatically calculated given path endpoints
  • Applying abstract programming principles to similar tasks (useful in low-volume applications or with slight variations in workpieces)
  • Open-source software used and supported by the community; preferred open-source licenses (i.e., BSD license) allow commercial use without restrictions
  • Reducing manufacturer "lock-in" by standardizing robot and sensor interfaces across many industrial platforms

The Open Source Robotics Foundation (OSRF) recently announced its plans to extend ROS capabilities to Qualcomm Snapdragon 600 processors for both Linux and Android operating systems. By integrating support for the ARM instruction set, architecture ROS opens the way for the development of smaller, more efficient robots with longer battery life.

A Unified Control Strategy

Broadly defined, robot controllers are a combination of hardware and software to program and control a single or multiple robots. They are responsible for coordinating all movements of the mechanical system, while also receiving environmental input through a number of sensors. They control robots’ vision, force sensing, and communication protocols.

Over the last years, controllers have significantly evolved. Before, they commonly used the same processor to handle different capabilities, such as vision and communication. Now, the multi-processor architecture is prevalent, enabling higher precision and throughput.

Smarter controller configurations have not been the only transformation, though. In the past, robot controllers were expected to absorb all functions within the work cell, playing an expansive role as managers of the entire automated architecture. Nowadays, however, robot controllers tend to play a secondary role due to the growing implementation of plant-wide, software-denominated, programmable logic controllers (PLCs).

PLCs are industrial computer control systems that continuously monitor the state of input devices and make decisions based upon a custom program to control the state of output devices. PLCs allow for the changing and replication of operations and processes while collecting and communicating vital information. Also, due to their modular nature, such controllers allow for selecting the type of input and output devices best suited for each application.

The use of PLC-based robotic controls eliminates the necessity of learning proprietary original equipment manufacturers' (OEM) control language, making training time considerably shorter. Other advantages of PLCs include:

  • Common programming controls (software, cables, etc.)
  • Common software interfaces
  • Common program backup/restore methodology
  • Common program documentation
  • Simplified troubleshooting and maintenance
  • Reduced panel footprint
  • Simplified training
  • Common spare parts
  • Common wire number/drawing numbering
  • Common part numbering scheme

Many facilities that already use PLC for controlling machines can take advantage of a unified control strategy as an easy way to incorporate robotics into their processes without the burden of dealing with a complex interface between different types of controllers.

This single, integrated approach for controlling both robots and machines has gained great acceptance among end-users. For this reason, robotics companies are increasingly engaged in developing innovative solutions that can be seamlessly integrated with existing PLC platforms.

The advent of PLC-based robotic controls has helped overcome the obstacles posed by unique OEM controllers and therefore enabled the concomitant use of multiple brands of robots. By fostering competition, PLC systems are making the robotics world more integrated and affordable.

Examples of Robot Software Innovation

I. Academic Research

The National Robotics Engineering Center (NREC) is an operating unit within Carnegie Mellon University’s Robotics Institute (RI), the world’s largest robotics research and development organization. The NREC works closely with government and industry clients to develop and mature robotic technologies from concept to commercialization.

Many NREC projects involve the development of innovative robot software. One example is the Autonomous Robotic Manipulation software, designed to enable robots to autonomously perform complex manipulation tasks. The goal is to develop a manipulator that carries out high-level tasks, interacts intelligently with its surroundings, adapts to real-world environments, and requires little supervision.

With the objective of ensuring the robustness and safety of robot software, the NREC has also created a toolkit called “Stress Testing for Autonomous Architectures”. This solution tests autonomy software and feeds potentially abnormal inputs into a system until it exhibits unsafe behavior. By doing so, it uncovers safety problems that are unlikely to arise during typical field testing but may occur when the system is actually put to use.

At MIT, important efforts are also underway to enhance robots’ functionality through the use of new software. In the field of object recognition, for instance, a recent project created a robot-vision algorithm, based on the Bingham distribution, which is 15 percent better than its best competitor at identifying familiar objects in cluttered scenes.

Because the Bingham distribution is a tool for reasoning probabilistically, it is particularly advantageous in contexts where information is inconsistent or unreliable. In cases where visual information is especially poor, the algorithm offers an improvement of more than 50 percent over the best alternatives.

When it comes to machine learning, researchers from MIT’s Laboratory for Information and Decision Systems (LIDS) and Computer Science and Artificial Intelligence Laboratory have developed a new reinforcement-learning algorithm that, for a wide range of problems, allows computer systems to find solutions much more efficiently than previous algorithms did.
It does so by identifying pertinent features in reinforcement-learning tasks and building a data structure tree that represents different combinations of features. It then investigates these arrangements to determine a policy’s success or failure. The algorithm’s competitive advantage lies in the ability of simply stop exploring combinations that consistently yield the same outcome.
The software, RLPy (for reinforcement-learning and Python, the programming language it uses), has been tested in robots that need to learn how to navigate an environment without knowing the potential obstacles in their ways.

II. Vision Guidance for Industrial Robots

Based in Bloomfield Hills, MI, Robotic Vision Technologies (RVT) specializes in vision-guided robotics. The company’s 3D vision guidance software eVisionFactory (eVF) has been widely adopted by the automotive, manufacturing, logistics, and consumer goods industries, as well as in government and defense projects.

Capable of locating, measuring, and identifying objects, eVF robots are “adaptive” and work better in real manufacturing environments. In other words, this innovative software enables “intelligent” decision-making and the prevention of costly mistakes.

In August 2014, RVT applied for a patent for a vision-based safety module that will allow operators to designate safety zones around a robot where it either slows down or ceases operations once an object is detected. Using a single overwatch camera, this innovative solution will replace the expensive light screens and other safety apparatus currently used to secure access to a manufacturing cell.

III. Yaskawa’s MotoPallet

Headquartered in West Carrollton, OH, Yaskawa Motoman is an American subsidiary of the Japanese company Yaskawa Electric Corporation. With nearly 300,000 Motoman robots, 10 million servos, and 18 million inverter drives installed globally, Yaskawa provides automation products and solutions for virtually every industry and robotic application.

Among the company’s innovative robot software, the MotoPallet EG stands out as an example of enhanced functionality and innovation. It enables users to determine the best possible palletizing solutions according to box orientation, shape, and size.

Capable of programming complicated pallet patterns, MotoPallet was the first software to allow for asynchronous movement, handling up to eight build stations, infeed and outfeed conveyors. The innovative solution also features Auto Place, a unique function that determines where to position robots for faster palletizing.

IV. Intera 3 for Baxter Robots

Created by Rethink Robotics, Baxter has redefined the ways robots can be used in manufacturing environments. The dual-arm humanoid was introduced in September 2012 and has since become an icon of human-robot collaboration.

Innovative software has been crucial in enabling Baxter’s widespread adoption. The robot requires no complex programming or costly integration. It presents behavior-based “common sense”, or intuitiveness. In other words, it is capable of sensing and adapting to a task or environment.

Ongoing R&D efforts and regular software releases promise to offer continuous improvements to Baxter’s capabilities. Such is the case for the 2014 Intera 3, which enables Baxter to perform with over twice the speed, precision, and motion quality of its flagship version. The following video compares Intera 3 and the 2013 version of the same software.

Rethink Robotics has also enhanced the open source capabilities of the Baxter Research Robot, which increasingly supports ROS-based applications. Examples include MoveIt!, for planning and testing trajectory algorithms in a virtual environment, and Gazebo, a physics-based robotics simulator.

Recent Trends in Robot Software

Among the most remarkable trends in robot software is the so-called “teachability”. R&D efforts are underway to make programming robots easier, if not unnecessary. Based in San Francisco, startup Brain Corporation has developed the BrainOS, an operating system that enables robots to learn by repetition.

The innovative company advocates that writing codes should no longer be a part of the robotic learning process. On the contrary, programming should start looking more like animal training. The idea is to repeatedly guide a robot to perform a given task and wait for it to start doing such a task by itself.  

In addition to potentially lowering the costs of intelligent robots, this new paradigm would considerably simplify robotic integration in all sorts of environments. No surprise that many industrial robot companies are currently working to add training by demonstration to their existing robots.

“Teachable” robots are only one example of how artificial intelligence can change the face of robotic software. The development of “common sense” or even “social intelligence” in robots are also promising areas for innovation.

Finally, software designed to enhance the collaboration between humans and robots should gain particular attention in the near future. Representing one of the major developments of industrial robotics in recent years, collaborative robots should become ever more present in manufacturing facilities, calling for advances in software that ensure efficiency, robustness, and safety.


Recent advances in software development point towards a bright future for the robotics industry. The consolidation of an open-source strategy that lowers costs and fosters the development of new applications, the integration of controllers into PLC systems, along with the development of more efficient, easier to use, teachable robots, are just a few examples of how software innovation is paving the way for the long-awaited robotic revolution.

Article Citation List



Charles R Goulding Attorney/CPA, is the President of R&D Tax Savers.

Andrea Albanese is a Manager with R&D Tax Savers.

Andressa Bonafé is a Tax Analyst with R&D Tax Savers.

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