Students learn about the concepts of accuracy and approximation as they pertain to robotics, gain insight into experimental accuracy, and learn how and when to estimate values that they measure. Students also explore sources of error stemming from the robot setup and rounding numbers.

Students work as physicists to understand centripetal acceleration concepts. They also learn about a good robot design and the accelerometer sensor. They also learn about the relationship between centripetal acceleration and centripetal force governed by the radius between the motor and accelerometer and the amount of mass at the end of the robot's arm. Students graph and analyze data collected from an accelerometer, and learn to design robots with proper weight distribution across the robot for their robotic arms. Upon using a data logging program, they view their own data collected during the activity. By activity end , students understand how a change in radius or mass can affect the data obtained from the accelerometer through the plots generated from the data logging program. More specifically, students learn about the accuracy and precision of the accelerometer measurements from numerous trials.

Students analyze the relationship between wheel radius, linear velocity and angular velocity by using LEGO(TM) MINDSTORMS(TM) NXT robots. Given various robots with different wheel sizes and fixed motor speeds, they predict which has the fastest linear velocity. Then student teams collect and graph data to analyze the relationships between wheel size and linear velocity and find the angular velocity of the robot given its motor speed. Students explore other ways to increase linear velocity by changing motor speeds, and discuss and evaluate the optimal wheel size and desired linear velocities on vehicles.

Students create four-legged walking robots and measure how far they travel across different types of surfaces. They design and create "shoes" to add to the robots' feet and observe the effect of their modifications on the net distance traveled across the various surface types. This activity illustrates how the specialized locomotive features of different species help them to survive or thrive in their habitat environments. The activity is best as an enrichment tool that follows a lesson that introduces the concept of biological adaptation to students.

This lesson explores the similarities between how a human being moves/walks and how a robot moves. This allows students to see the human body as a system, i.e., from the perspective of an engineer. It shows how movement results from (i) decision making, i.e., deciding to walk and move, and (ii) implementing the decision by conveying the decision to the muscle (human) or motor (robot).

Students learn more about assistive devices, specifically biomedical engineering applied to computer engineering concepts, with an engineering challenge to create an automatic floor cleaner computer program. Following the steps of the design process, they design computer programs and test them by programming a simulated robot vacuum cleaner (a LEGO® robot) to move in designated patterns. Successful programs meet all the design requirements.

6.270 is a hands-on, learn-by-doing class, in which participants design and build a robot that will play in a competition at the end of January. The goal for the students is to design a machine that will be able to navigate its way around the playing surface, recognize other opponents, and manipulate game objects. Unlike the machines in Design and Manufacturing I (2.007), 6.270 robots are totally autonomous, so once a round begins, there is no human intervention.
The goal of 6.270 is to teach students about robotic design by giving them the hardware, software, and information they need to design, build, and debug their own robot. The subject includes concepts and applications that are related to various MIT classes (e.g. 6.001, 6.002, 6.004, and 2.007), though there are no formal prerequisites for 6.270.

Students use ultrasonic sensors and LEGO© MINDSTORMS© NXT robots to emulate how bats use echolocation to detect obstacles. They measure the robot's reaction times as it senses objects at two distances and with different sensor threshold values, and again after making adjustments to optimize its effectiveness. Like engineers, they gather and graph data to analyze a given design (from the tutorial) and make modifications to the sensor placement and/or threshold values in order to improve the robot's performance (iterative design). Students see how problem solving with biomimicry design is directly related to understanding and making observations of nature.

Student teams design their own booms (bridges) and engage in a friendly competition with other teams to test their designs. Each team strives to design a boom that is light, can hold a certain amount of weight, and is affordable to build. Teams are also assessed on how close their design estimations are to the final weight and cost of their boom "construction." This activity teaches students how to simplify the math behind the risk and estimation process that takes place at every engineering firm prior to the bidding phase when an engineering firm calculates how much money it will take to build the project and then "bids" against other competitors.

Students learn about the similarities between the human brain and its engineering counterpart, the computer. Since students work with computers routinely, this comparison strengthens their understanding of both how the brain works and how it parallels that of a computer. Students are also introduced to the "stimulus-sensor-coordinator-effector-response" framework for understanding human and robot actions.

Students will create the word STEM using binary code on a candy cane. They will have the option of doing this on a piece of paper or with Legos.

At its core, the LEGO MINDSTORMS(TM) NXT product provides a programmable microprocessor. Students use the NXT processor to simulate an experiment involving thousands of uniformly random points placed within a unit square. Using the underlying geometry of the experimental model, as well as the geometric definition of the constant π (pi), students form an empirical ratio of areas to estimate a numerical value of π. Although typically used for numerical integration of irregular shapes, in this activity, students use a Monte Carlo simulation to estimate a common but rather complex analytical form the numerical value of the most famous irrational number, π.

Posed with a paradigmatic engineering problem, students consider and explore mathematical algorithms and/or geometric concepts to devise possible solutions. The problem: How should a robotic vacuum move in order to best clean a floor of unknown shape and dimensions? They grapple with what could be a complex problem by brainstorming ideas, presenting the best idea for a solution and analyzing all presented solutions, and then are introduced to an elegant solution. Rather than elaborately calculating the most efficient route and keeping track of which tiles the robot has visited, a random number generator determines which direction the robot will take when it hits a barrier. Students are able to visually confirm how an unfamiliar programming concept (a random number generator) can make for a simple and efficient program that causes an NXT robot (that is suitably equipped) to clean a bare floor. Then students think of other uses for random numbers.

Students continue their exploration of the human senses and their engineering counterparts, focusing on the auditory sense. Working in small groups, students design, create and run programs to control the motion of LEGO® TaskBots. By doing this, they increase their understanding of the use and function of sound sensors, gain experience writing robot programs, and reinforce their understanding of the sensory process.

Students gain a deeper understanding of how sound sensors work through a hands-on design challenge involving LEGO MINDSTORMS(TM) NXT taskbots and sound sensors. Student groups each program a robot computer to use to the sound of hand claps to control the robot's movement. They learn programming skills and logic design in parallel. They experience how robots can take sensor input and use it to make decisions to move and turn, similar to the human sense of hearing. A PowerPoint® presentation and pre/post quizzes are provided.

Students learn about and practice converting between fractions, decimals and percentages. Using a LEGO® MINDSTORMS® NXT robot and a touch sensor, each group inputs a fraction of its choosing. Team members convert this same fraction into a decimal, and then a percentage via hand calculations, and double check their work using the NXT robot. Then they observe the robot moving forward and record that distance. Students learn that the distance moved is a fraction of the full distance, based on the fraction that they input, so if they input ½, the robot moves half of the original distance. From this, students work backwards to compute the full distance. Groups then compete in a game in which they are challenged to move the robot as close as possible to a target distance by inputting a fraction into the NXT bot.

Students learn about nanocomposites, compression and strain as they design and program robots that compress materials. Student groups conduct experiments to determine how many LEGO MINDSTORMS(TM) NXT motor rotations it takes to compress soft nanocomposites, including mini marshmallows, Play-Doh®, bread and foam. They measure the length and width of their nanocomposite objects before and after compression to determine the change in length and width as a function of motor rotation.

Student teams design and create LEGO® structures to house and protect temperature sensors. They leave their structures in undisturbed locations for a week, and regularly check and chart the temperatures. This activity engages students in the design and analysis aspects of engineering.

Students quantify the percent of light reflected from solutions containing varying concentrations of red dye using LEGO© MINDSTORMS© NXT bricks and light sensors. They begin by analyzing a set of standard solutions with known concentrations of food coloring, and plot data to graphically determine the relationship between percent reflected light and dye concentration. Then they identify dye concentrations for two unknown solution samples based on how much light they reflect. Students gain an understanding of light scattering applications and how to determine properties of unknown samples based on a set of standard samples.

Students discover the mathematical constant phi, the golden ratio, through hands-on activities. They measure dimensions of "natural objects"—a star, a nautilus shell and human hand bones—and calculate ratios of the measured values, which are close to phi. Then students learn a basic definition of a mathematical sequence, specifically the Fibonacci sequence. By taking ratios of successive terms of the sequence, they find numbers close to phi. They solve a squares puzzle that creates an approximate Fibonacci spiral. Finally, the instructor demonstrates the rule of the Fibonacci sequence via a LEGO® MINDSTORMS® NXT robot equipped with a pen. The robot (already created as part of the companion activity, The Fibonacci Sequence & Robots) draws a Fibonacci spiral that is similar to the nautilus shape.