SB
Spandana Bala
ML Engineer
Hyderabad · India
Now at · Infosys
Foundations · Build your engineering starting point
Foundations of Robotics
Frames, Sensors, Mechanics & Feedback
Understand how a robot senses, moves and responds before building its autonomy. Connect basic mechanics, coordinate frames, sensor sampling and feedback through practical calculations and simulation. Build a small sampled-control project and explain its units, limits and timing assumptions.
10 practical modules
Three guided projects
One independently assessed foundation capstone
A readiness review for your next learning step
RoboEdify track record
10,000+
alumni transformed
1,000+
hiring partners
4.8/5
average class rating
87%
placed in 6 months
10+
years of training
Where our AI alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer
What will this foundation help you understand?
Robotics foundations describe the relationship between a physical system, its sensors, its actuators and the software that controls it. This course develops the reasoning needed before advanced ROS 2, navigation or robot-learning work.
Learning approach
Build understanding through small tasks you can explain.
Each module connects a concept to a practical exercise. You predict a result, run the task, inspect what happened and correct a mistake. The final review checks your own reasoning and working project rather than attendance alone.
Learning format
Learn through guided practice and individual feedback.
The program combines mentor-led explanations, exercises, project work and support sessions. An advisor can explain the current on-campus, online or working-professional format and the learning setup before enrollment.
On-campusLive onlineWorking-professional format
Who should join
Start at the level your current skills support.
No prior robot ownership or ROS experience is required. You should be comfortable with basic Python and school-level algebra. Complete Foundations of Engineering Computing or demonstrate equivalent readiness. The course introduces bounded C and memory examples without assuming full firmware competence.
This course is suitable for students, graduates and working professionals who want to strengthen the relevant foundations. A diagnostic helps avoid repeating skills you can already demonstrate.
Learning outcomes
Leave with foundations you can demonstrate.
Describe a robot systemConnect sensors, computation, actuation and environment.
Use coordinate framesTransform a point and check direction and units.
Estimate basic motion requirementsReason about torque, speed and actuator limits.
Interpret sampled sensorsIdentify noise, stale data and sampling limitations.
Build a bounded feedback modelMeasure tracking, overshoot and saturation.
Explain a system handoffDocument model assumptions and demonstrated limitations.
Entry readiness
What you should be able to do before you start.
ComputingRun a Python script and inspect a numeric plot.
MathematicsUse algebra, basic trigonometry and unit conversions.
Engineering curiosityRead a simple block diagram and describe cause and effect.
Course curriculum
Ten modules, from first principles to a working foundation project.
01
Robot systems and interfaces
Foundation
+
01
Robot systems and interfaces
FoundationTopics
•Robot types
•Sensing/computation/actuation
•Open versus closed loop
•Units
•System boundaries
Hands-on lab
Draw a small mobile-robot or actuator system and label every signal.
Failure scenario you solve
A command is interpreted as physical position even though it represents motor speed.
You build
System diagram and signal contract.
Assessed outcome: Explain the meaning and units of each interface.
02
Engineering mathematics and units
Foundation
+
02
Engineering mathematics and units
FoundationTopics
•Scalars/vectors
•Coordinates
•Trigonometry
•Dimensional checks
•Rate and change
Hands-on lab
Calculate a simple displacement and speed budget with unit checks.
Failure scenario you solve
Millimeters are processed as meters.
You build
Worked calculations and unit tests.
Assessed outcome: Detect a unit mismatch before tuning the model.
03
Frames and basic kinematics
Foundation
+
03
Frames and basic kinematics
FoundationTopics
•Local/world frames
•Planar rotations
•Translations
•Forward kinematics
•Frame composition
Hands-on lab
Transform a sensor point between two planar frames and compare with a numerical result.
Failure scenario you solve
The transform is applied in the wrong direction.
You build
Frame diagram and transformation notebook.
Assessed outcome: Explain coordinate direction and verify a known reference point.
04
Mechanics and actuators
Foundation
+
04
Mechanics and actuators
FoundationTopics
•Force
•Torque
•Gearing
•Speed/load tradeoffs
•Inertia intuition
•Saturation
•Actuator selection assumptions
Hands-on lab
Estimate requirements for a benign simulated joint and compare against a supplied actuator envelope.
Failure scenario you solve
A model assumes unlimited torque and predicts motion the actuator cannot produce.
You build
Actuator budget and assumption sheet.
Assessed outcome: Explain why a requested motion exceeds the stated limit.
05
Sensors and sampling
Foundation
+
05
Sensors and sampling
FoundationTopics
•Measurement units
•Noise
•Bias
•Sampling rate
•Aliasing awareness
•Quantization
•Timestamps
•Missing data
Hands-on lab
Sample a synthetic signal at different rates and inject stale or biased readings.
Failure scenario you solve
A smooth-looking sampled signal conceals a faster variation.
You build
Signal plots and sensor-quality checks.
Assessed outcome: Distinguish measured data from the underlying physical signal.
06
Feedback and discrete control
Foundation
+
06
Feedback and discrete control
FoundationTopics
•Setpoint/error
•Proportional feedback
•Sampled updates
•Stability intuition
•Overshoot
•Settling
•Saturation
Hands-on lab
Simulate a bounded proportional controller and compare several settings under the same conditions.
Failure scenario you solve
Increasing gain produces oscillation rather than better tracking.
You build
Response plots and controlled comparison.
Assessed outcome: Explain the observed tradeoff without claiming a formal stability proof.
07
Embedded computing and memory awareness
Foundation
+
07
Embedded computing and memory awareness
FoundationTopics
•MCU versus host
•Basic C types
•Arrays
•Bounded buffers
•Input validation
•Ownership
•Overflow
Hands-on lab
Trace and repair a small supplied C buffer example with host-based tests.
Failure scenario you solve
An incoming sample overwrites a buffer boundary.
You build
Corrected teaching example and boundary tests.
Assessed outcome: Identify the bounds and explain how invalid input is handled.
08
Timing and system integration
Foundation
+
08
Timing and system integration
FoundationTopics
•Sensor/control update periods
•Computation delays
•Task budgets
•Stale commands
•Defined fallback
Hands-on lab
Combine a sensor model, controller and limited actuator, then introduce delay or dropout.
Failure scenario you solve
The controller acts on old data as if it were fresh.
You build
Integrated simulation and timing/fault report.
Assessed outcome: Identify a stale-input condition and demonstrate the defined response.
09
Independent robotics foundation capstone
Capstone
+
09
Independent robotics foundation capstone
CapstoneTopics
•Frozen model
•Frames
•Units
•Sampled control
•Actuator limits
•Fault scenarios
Hands-on lab
Deliver a simulated sensor-to-actuator controller with tracking plots and explicit limits.
Failure scenario you solve
A demonstration works nominally but fails after a sample dropout or saturation event.
You build
Simulation, assumptions, response metrics and failure report.
Assessed outcome: Reproduce normal/fault behavior and explain the model’s limits.
10
Readiness review and technical explanation
Readiness review
+
10
Readiness review and technical explanation
Readiness reviewTopics
•Frame calculations
•Control plots
•Data freshness
•Personal contribution
•Pathway choice
Hands-on lab
Explain the project and diagnose a new unit, sign or timing error.
Failure scenario you solve
A learner tunes around a frame error instead of correcting it.
You build
Project summary and individual feedback.
Assessed outcome: Show readiness for further robotics or firmware preparation.
Scope note: This course does not teach a complete ROS 2 navigation stack, production firmware or a deployable autonomous robot. It provides the physical and computational reasoning those courses build on.
Tools and methods
Use a focused toolkit that supports understanding.
Python, NumPy and plottingModel signals, transforms and feedback.
A basic C compilerInspect bounded memory examples.
A qualified lightweight simulatorRun the sensor/control/actuator model.
Git and testsReproduce calculations and fault cases.
The exact software setup is qualified before teaching. Supplied examples and small datasets keep the core accessible; no physical robot, FPGA board or paid cloud subscription is required for the stated foundation assessment.
Projects and portfolio
Three guided projects and a foundation capstone.
Guided projects develop across the modules; they are part of the course rather than additional promises of production experience.
Guided project 1
Frame transformation notebook
Locate a sensor observation in a different frame and validate known points.
Portfolio evidence
Frame diagramCalculationsTests
Guided project 2
Sensor sampling investigation
Compare noise, bias, stale samples and different sampling rates.
Portfolio evidence
Signal plotsQuality checksFindings
Guided project 3
Bounded feedback controller
Compare tracking under actuator limits and delay.
Portfolio evidence
SimulationResponse metricsFault cases
Capstone
A sampled sensor-to-actuator control model
Build a simple simulated feedback system with an explicit frame/unit contract, sensor update behavior and actuator limits. Compare nominal tracking with delay, dropout and saturation cases.
What you submit
- •A clear problem statement and assumptions.
- •Your code, calculations or simulation files.
- •Tests covering the specified normal and failure cases.
- •A result report with units, counts or metrics as applicable.
- •A readable reproduction guide and individual explanation.
What the assessor checks
- •Declare plant assumptions, coordinate conventions, input/output units and actuator limits.
- •Use identical test conditions when comparing controller settings.
- •Report tracking error, overshoot and saturation, and retain failed cases.
- •Explain the defined stale-data response and separate simulation observations from physical validation.
Assessment and completion
Demonstrate understanding before moving forward.
RoboEdify · Certificate of Completion
Foundations of Robotics
Presented to
Learner name
Awarded for completing the foundation exercises and independently explaining an assessed project within the course scope.
Manikanta Kona
Founder · RoboEdify
ROBO
EDIFY
CERT
EDIFY
CERT
30%
Module exercises
20%
Practical checkpoints
35%
Foundation capstone
15%
Individual explanation and debugging
The proposed completion standard is 70% overall, at least 60% separately in the capstone and individual review, and completion of all mandatory project requirements. Feedback identifies specific gaps for remediation and reassessment.
The credential records foundation completion. It does not replace specialist training, employer assessment or external certification.
Your next learning step
Use the foundation to choose a focused engineering pathway.
Supports Robotics Software Engineer and Embedded Systems & Firmware preparation. Drone, humanoid and autonomous-driving courses still require additional software, control and domain readiness.
Your next-course recommendation is based on demonstrated readiness. Recognized foundation work can satisfy matching preparation outcomes, but each advanced course still checks its specific prerequisites. You do not need to take all four foundations unless your chosen pathway requires them.
Career support
Build your first technical portfolio and plan your next step.
Across RoboEdify’s career programs, support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. This foundation course focuses on project feedback and progression readiness. The shared hiring-partner network includes Infosys, TCS, Deloitte, Accenture, Cognizant, NTT Data and Capgemini.
01 / PORTFOLIO
Evidence from your own work.
For this foundation course, your portfolio starts with the assessed project, a clear explanation of your work and the corrections you made after feedback.
02 / PROFILE
Profile and resume preparation.
A reviewed technical project summary, a readable repository and resume statements grounded in your contribution.
03 / INTERVIEWS
Interview practice and introductions.
Technical interview practice, with role-fit introductions where available.
RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.
Institute leadership
Meet the team behind RoboEdify.
MK
Manikanta Kona
Founder, RoboEdify · Enterprise AI Architect
Enterprise AI · Agentic Systems · LLM Platforms · Robotics & Edge AI
15 yrs
ENTERPRISE AI
2,400+
LEARNERS
4.9 /5
RATING
Manikanta brings 15 years of enterprise platform architecture experience from AT&T, Salesforce, Cox Communications and Broadcom. His background includes enterprise platform and AI rollouts for Fortune-500 banks, telcos and insurers, and production agentic-AI deployments for governed case handling.
Education: M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, RoboEdify · Implementation & Delivery Lead
Enterprise automation · Deployment · Evaluation evidence · Delivery governance
10 yrs
IMPLEMENTATION & DELIVERY
1,800+
LEARNERS
4.8 /5
RATING
Ravi leads RoboEdify's implementation and delivery practice. His background spans enterprise automation programs, deployment, evaluation evidence and delivery governance.
Industry voices from RoboEdify’s AI programs
What employers say about RoboEdify’s AI and robotics graduates.
The following testimonials retain their original program context. They describe AI, robotics and enterprise-program experience, rather than outcomes from this foundation course.
RoboEdify grads ramp 40% faster on applied AI projects than typical hires. Best AI engineering pipeline in India.
Aakash Mehta, Partner Programme Lead, Microsoft
We've onboarded 80+ RoboEdify alumni in 18 months. Lowest ramp time we've seen for ML plus AI agent practices.
Anita Sharma, Senior Manager, Deloitte
The programme is comprehensive — predictive ML, LLM systems, plus agentic and robotics work. Grads come pre-trained for enterprise.
Rahul Bhatt, Solutions Lead, Mphasis
Their agent + robotics track produces engineers who ship production-grade perception and control code on day one. Genuinely rare.
Deepak Pillai, Senior Architect, TCS
What sets RoboEdify apart is the simulation-to-hardware layer baked into the AI track. Our clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their fundamentals prep is rigorous, and the capstone with a real deployed system and safety case is what closes interviews for us.
Vikram Iyer, Director, Infosys
RoboEdify's AI grads get models into production twice as fast in the first 90 days. Our internal metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best AI + robotics pipeline we've sourced from in India. Their projects are production work, not toy code.
Karthik Subramanian, Engineering Director, Cognizant
Strong ML and edge-deployment foundation. Their grads need almost zero ramp time on enterprise engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ RoboEdify alumni across our AI and automation teams. Strong fundamentals, sharp on the agent stack.
Sanjay Verma, Talent Director, IBM
ITOM + Predictive Intelligence is exactly the talent gap we've been struggling to close. RoboEdify is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their AI track delivers engineers who navigate data, models and integrations on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ RoboEdify graduates for our AI practice. Strong coding, strong ML depth, sharp on the agent layer.
Geetha Pillai, Talent Acquisition Lead, Cyient
RoboEdify grads who blend robotics with Azure OpenAI land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
AI alumni across RoboEdify
Meet alumni featured in our AI programs.
NV
Naveen Vedala
AI Agent Engineer
Hyderabad · India
Now at · TCS
TA
Tejashwini Addla
Simulation Engineer
Hyderabad · India
Now at · Deloitte
TD
Tharunesh Dillikar
Robotics Software Engineer
Seattle · United States
Now at · Accenture
MM
Mujahed Mohammed
Edge AI Engineer
Hyderabad · India
Now at · Accenture
BK
Bhargav Kumar Murala
Physical AI Engineer
Hyderabad · India
Now at · Capgemini
SL
Sai Manasa Leburi
Autonomous Systems Engineer
New York · United States
Now at · NTT Data
RD
Rahul Dhamma
Robot Learning Engineer
Hyderabad · India
Now at · Cognizant
Our locations
Come chat with us—on campus or online.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 8142998866
US desk
+1 256 388 7766
Opening hours
Mon–Sun · 7 AM–9 PM
Online
Global
Live online classes and mentorship, with working-professional learning options. The foundation uses the software or simulation setup described in its tools section.
Format
Live online + mentorship
Options
Working-professional
Learning support
Support when you need to catch up.
Freeze your seat for up to 90 days and rejoin the next class at no extra fee. TAs run catch-up sessions every Saturday, and recordings of every live session are available for the lifetime of your account.
FAQs
Questions about prerequisites, tools and completion.
Do I need to buy a robot?
No. The core exercises use calculations and simulation.
Will I learn ROS 2 here?
You learn the underlying frames, sensing and control concepts. Full ROS 2 software development belongs to Robotics Software Engineer.
Is this mainly mechanical or software training?
It connects both at foundation level: model the physical system, then explain how software samples and controls it.
Can I move directly to humanoids afterward?
This is one foundation. Humanoid work also requires stronger dynamics, optimization, control and robotics-software competence.
Is this a job-ready specialist course?
This is foundation training. It develops prerequisites and a first technical project; specialist-role preparation belongs to the relevant advanced course.
Can experienced learners skip material?
A practical diagnostic can recognize skills you already demonstrate. Progression still depends on the readiness required by your chosen pathway.
How is learning organized?
The course combines explanations, guided exercises, a project and individual feedback. An advisor can explain the current class format before enrollment.
What if I need to pause or catch up?
You can freeze your seat for up to 90 days and rejoin the next class at no extra fee. Saturday catch-up sessions and live-session recordings support learning. Recordings remain available for the lifetime of your account.
Are placement results on the page from this foundation?
No. The shared figures and AI alumni stories come from RoboEdify’s Physical AI page. They are not outcomes specific to this foundation course.
How can I ask about fees and the learning setup?
Contact a course advisor to discuss the current offering, preparation needs, software requirements, fees and support terms.
Still have a question?
Find the right foundation for your next step.
One million AI-native professionals by 2027.
Tell us what you already know and which engineering pathway interests you. We’ll help you identify the foundations to strengthen and the practical work to begin with.
Plan your learning
- Course
- Foundations of Robotics
- Preparation
- Computing readiness (Foundations of Engineering Computing or equivalent)
- Level
- Foundation
- Curriculum
- 10 modules
Confirm your intake dates, delivery mode, fees, assessment and practical access with RoboEdify before enrolling. Course content describes the learning scope; an enquiry does not reserve a seat.