Robotics & Autonomous Systems · Practical career preparation

Robotics Software Engineer

ROS 2, Navigation & Robot Integration

Build robot software that knows its state—and handles the unexpected. Develop ROS 2 applications, integrate sensors, work with robot frames and build a navigation task with observable behavior. Learn to investigate stale data, failed actions and localization errors through simulation, recorded data and repeatable tests.

12 core modules Diagnostic-based preparation Three guided projects One independently assessed capstone

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

Microsoft

Amazon

Salesforce

ServiceNow

Deloitte

Infosys

Accenture

TCS

Wipro

Capgemini

Cognizant

HCL

Direct answer

What does this engineering pathway involve?

A robotics software engineer connects sensing, state estimation, planning and execution into a working robot application. The work includes software architecture, interfaces, runtime diagnosis and testing—not just running a navigation demo. This course uses a mobile robot as the main integration path, with a bounded manipulation lab for additional context.

Learning approach

Learn from behavior you can inspect and failures you can reproduce.

Each practical task includes an explicit contract, a working baseline and a failure to investigate. You use logs, plots or recordings to explain the result, then test your correction. The final assessment includes an unfamiliar debugging task so that a rehearsed demonstration is not the only evidence of competence.

Learning format

Learn through classes, labs and individual feedback.

The program combines live mentor-led classes, guided labs, project work and support sessions. An advisor can explain the current on-campus, online or working-professional format and the course-specific lab arrangements before enrollment.

Simulation, replay and physical demonstrations are identified separately. The page does not imply that a simulated result has already been reproduced on hardware.

On-campusLive onlineWorking-professional format

Who should join

Match the starting point to your existing skills.

Graduates can enter through programming and robotics-mathematics preparation. Working engineers demonstrate equivalent foundations through a diagnostic. The same core assesses code, frame reasoning, runtime behavior and independent debugging; prior employment does not waive these outcomes.

Graduates and working engineers follow the same practical exit requirements. Previously demonstrated foundations can be recognized; broader gaps receive a separate learning plan before the core.

Learning outcomes

Build skills you can demonstrate.

Develop ROS 2 softwareBuild C++/Python nodes with explicit interfaces and tests.

Maintain correct frames and timeDiagnose transform, synchronization and freshness problems.

Integrate robot sensingRecord and inspect camera, range and odometry data.

Configure navigationEvaluate mapping, localization and task execution.

Handle failure deliberatelySupport cancellation, bounded retries and visible terminal states.

Deliver reproducible evidenceArchive a working application, recordings and scenario results.

Preparation areas

What you practise before the core.

C++ and PythonFunctions, classes, ownership, collections, numerical scripts and basic unit tests.

Linux, Git and buildsUse the terminal, maintain a repository and reproduce a small build.

Robotics mathematicsVectors, matrices, coordinate transforms, basic probability and feedback concepts.

Digital and sensor foundationsInterpret timestamps, sampling, units and simple interface behavior.

Graduates and working engineers follow the same practical exit requirements. Previously demonstrated foundations can be recognized; broader gaps receive a separate learning plan before the core.

Course curriculum

Twelve modules, from foundations to an independent engineering assessment.

Each module includes a practical task, a failure scenario, deliverables and an assessed outcome.

Engineering workflow and robotics foundations

Engineering core

Topics

•Linux workspaces

•Git

•C++ ownership and Python scripts

•Builds and tests

•Module boundaries

•Reproducible configuration

Hands-on lab

Build and test a small sensor-processing package from a clean workspace.

Failure scenario you solve

The application works only in the author’s shell because dependencies and configuration were not recorded.

You build

Build manifest, tested package and reproduction guide.

Assessed outcome: Reproduce the package and explain data ownership in its core callback.

ROS 2 communication and execution

Engineering core

Topics

•Nodes and interfaces

•Topics

•QoS

•Services

•Actions

•Executors

•Callback groups

•Parameters

•Discovery

Hands-on lab

Implement a sensor observer and cancellable action; test incompatible QoS and concurrent callbacks.

Failure scenario you solve

A publisher is active but its subscriber receives nothing, or a blocking callback prevents cancellation.

You build

ROS 2 packages, interface contract and execution tests.

Assessed outcome: Identify a communication/execution failure from observable evidence.

Robot descriptions, transforms and kinematics

Engineering core

Topics

•URDF/Xacro

•Links/joints

•Tf2

•Coordinate conventions

•Forward kinematics

•Joint limits

•Collision geometry

Hands-on lab

Model a mobile base and sensor; validate a known landmark transform numerically and in RViz.

Failure scenario you solve

A sensor frame is rotated or scaled incorrectly and the obstacle appears in the wrong place.

You build

Robot description, transform checks and annotated frame tree.

Assessed outcome: Find an injected frame error without compensating for it in downstream tuning.

Simulation and controller interfaces

Engineering core

Topics

•Compatible ROS/Gazebo pairing

•Simulation clock

•Ros2_control

•Joint state and command interfaces

•Lifecycle bring-up

Hands-on lab

Bring up a supplied base model and inspect controller activation, command ownership and stopping.

Failure scenario you solve

Two components compete for a command interface or a controller never becomes active.

You build

Launch files, controller configuration and bring-up checklist.

Assessed outcome: Explain who owns each command and demonstrate a controlled stop in simulation.

Sensors, calibration and replay

Engineering core

Topics

•Camera/range/IMU/odometry inputs

•Calibration

•Timestamps

•Rosbag2

•Missing/out-of-order data

•Data manifests

Hands-on lab

Record and replay a multi-sensor route, then delay or remove a stream and detect stale input.

Failure scenario you solve

A replay uses the wrong time configuration and silently changes the derived behavior.

You build

Recorded dataset, calibration notes and freshness tests.

Assessed outcome: Reproduce a failure with the intended time model and identify invalid observations.

State estimation and localization

Engineering core

Topics

•Odometry integration

•Uncertainty

•Prediction/update intuition

•Robot_localization concepts

•Map/odom/base frames

•Drift metrics

Hands-on lab

Compare wheel-only and fused estimates on a fixed recorded route with held-out segments.

Failure scenario you solve

An incorrect covariance makes a noisy input dominate the estimate.

You build

Estimator configuration and drift/error report.

Assessed outcome: Explain a fusion choice and report error without tuning on the held-out route.

Mapping and Nav2 integration

Engineering core

Topics

•SLAM versus localization

•Occupancy maps

•Robot footprint

•Costmaps

•Planners/controllers

•Path and goal checks

Hands-on lab

Build a map and run a navigation baseline using a qualified Nav2 configuration.

Failure scenario you solve

An inaccurate footprint allows the planned path to pass through a gap the robot cannot fit.

You build

Map, navigation configuration and scenario traces.

Assessed outcome: Connect a navigation failure to geometry, localization or planning evidence.

Behavior trees and task recovery

Engineering core

Topics

•Task states

•Preconditions

•Actions

•Cancellation

•Recovery branches

•Retry bounds

•Lifecycle restart

•Terminal outcomes

Hands-on lab

Implement waypoint inspection with cancellation, a blocked-path response and a maximum retry count.

Failure scenario you solve

Recovery repeats indefinitely while the operator sees no reason for the failure.

You build

Task behavior tree/state model and fault tests.

Assessed outcome: Stop repeated failure, report its cause and preserve operator cancellation.

Manipulation and motion-planning introduction

Engineering core

Topics

•MoveIt planning scene

•Inverse kinematics

•Reachability

•Collision checks

•Trajectory limits

•Plan versus execute

Hands-on lab

Use a supplied simulated arm for one collision-aware reach/pick sequence with a blocked target.

Failure scenario you solve

A stale planning scene makes a valid-looking trajectory inconsistent with the environment.

You build

Planning-scene configuration and bounded manipulation report.

Assessed outcome: Explain why a computed path alone does not establish successful execution.

Integration testing and deployment discipline

Engineering core

Topics

•Component and launch tests

•Logs

•Profiling

•CPU/latency budgets

•Containers

•Diagnostics

•Hardware-transfer assumptions

Hands-on lab

Create a repeatable scenario runner and diagnose a slow callback or stale command.

Failure scenario you solve

A short demo passes while sustained load makes sensor results too old to use.

You build

Test runner, timing report and transfer checklist.

Assessed outcome: Distinguish observed timing from a formal real-time guarantee.

Independent mobile-robot capstone

Capstone

Topics

•Frozen task and map

•Held-out scenarios

•Sensor validity

•Navigation

•Bounded recovery

•Reproducible evaluation

Hands-on lab

Deliver a simulated indoor inspection robot that visits checkpoints, records results and handles blocked paths and stale sensing.

Failure scenario you solve

A route succeeds once but fails after a changed starting pose or interrupted action.

You build

Application repository, recordings, scenario report and demonstration.

Assessed outcome: Complete required scenarios, explain failures and reproduce an assessor-selected case.

Technical interviews and portfolio defense

Career preparation

Topics

•ROS graph diagnosis

•C++ reasoning

•Transforms

•Navigation reports

•Personal contribution

•Live debugging

Hands-on lab

Explain the capstone and diagnose an unfamiliar QoS, frame or task-state defect.

Failure scenario you solve

A learner can launch the stack but cannot identify where an incorrect state originated.

You build

Technical README, project summary and individual feedback.

Assessed outcome: Trace a failure across interfaces and defend the correction.

Scope note: The capstone develops mobile-robot software integration. Advanced perception research, full manipulation-stack ownership, drone flight, humanoid control and vehicle autonomy are separate specializations. No industrial safety certification is implied.

Tools and methods

Use a focused, compatible engineering stack.

ROS 2, C++ and PythonImplement nodes, interfaces and robot behaviors.

RViz, tf2 and rosbag2Inspect frames and reproduce recorded behavior.

Gazebo and ros2_controlRun the qualified robot model and controller interfaces.

Nav2Build the mobile navigation core.

MoveIt 2Complete the bounded manipulation lab.

Linux, Git and testsKeep the application reproducible.

Core work uses simulation and recorded data. The candidate baseline retains ROS 2 Jazzy with a compatible OS, Gazebo and package set; the complete environment is pinned after qualification. Physical-robot access is a separately specified supervised extension, not an assumed entitlement.

Projects and portfolio

Three guided projects and an independent capstone.

The guided projects develop across the curriculum and feed the final assessment.

Guided project 1

Observable ROS 2 sensor and action system

Build a publisher/observer and a cancellable action with explicit freshness and terminal-state handling.

Portfolio evidence

ROS packagesQoS testsCancellation traces

Guided project 2

Mapping and navigation investigation

Map a fixed environment, evaluate localization and inspect a blocked or geometrically invalid route.

Portfolio evidence

MapEstimation reportNavigation configuration

Guided project 3

Bounded manipulation scene

Plan and execute one simulated arm task while handling a changed obstacle scene.

Portfolio evidence

MoveIt sceneTrajectory checksFailure report

Capstone

Indoor inspection robot with observable recovery

Integrate a supplied mobile-robot model, sensor processing, localization and navigation into a repeatable inspection task. Use unseen starts and obstacle cases to evaluate the application, with operator cancellation and visible failure outcomes.

What you submit

•A frozen scope and acceptance checklist.
•ROS 2 packages, robot configuration, recorded failures and navigation evaluation.
•Source/configuration files and a readable reproduction guide.
•A failure report showing cause, correction and recheck.
•A demonstration with complete scenario/trial results and limitations.
•An individual walkthrough and live debugging assessment.

What the assessor checks: The project meets the agreed task contract, reports every required test and preserves the distinction between simulation and hardware evidence. You explain failures and your own contribution. Unsupported capability claims, omitted failed trials and an edited demonstration alone do not satisfy the assessment.

Assessment and completion

Demonstrate what you can do.

RoboEdify · Certificate of Completion

Robotics Software Engineer

Presented to

Learner name

Awarded for completing the practical assessments and independently defending the course capstone within its documented scope.

Manikanta Kona

Founder · RoboEdify

ROBO
EDIFY
CERT

30%

Module labs

20%

Practical checkpoints

35%

Capstone

15%

Individual debugging and defense

The proposed completion standard is 70% overall, at least 60% separately in the capstone and individual defense, and completion of all mandatory practical requirements. Feedback identifies the work needed for remediation and reassessment.

This is a proposed RoboEdify course credential. It is not a regulatory operating permission, professional licence or external standards certification.

Career preparation

Prepare for relevant engineering work with evidence you can explain.

Robotics Software Trainee / Junior EngineerDevelop ROS 2 applications and diagnose integration problems.

AMR Navigation / Integration TraineeBuild navigation, sensing and task-execution evidence.

Robotics Test / Simulation EngineerCreate reproducible scenario tests and failure reports.

Career preparation includes a reviewed repository, a technical project summary, evidence-based resume statements and individual interview practice. Course completion does not establish senior-role eligibility; employers set their own experience and qualification requirements.

Career support

Build your portfolio. Prepare your profile. Practise your interviews.

Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. The shared hiring-partner network includes Infosys, TCS, Deloitte, Accenture, Cognizant, NTT Data and Capgemini.

01 / PORTFOLIO

Evidence from your own work.

For this course, your portfolio centres on ROS 2 packages, robot configuration, recorded failures and navigation evaluation you complete.

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.

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.

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 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.

Spandana Bala

ML Engineer

Hyderabad · India

Now at · Infosys

Naveen Vedala

AI Agent Engineer

Hyderabad · India

Now at · TCS

Tejashwini Addla

Simulation Engineer

Hyderabad · India

Now at · Deloitte

Tharunesh Dillikar

Robotics Software Engineer

Seattle · United States

Now at · Accenture

Mujahed Mohammed

Edge AI Engineer

Hyderabad · India

Now at · Accenture

Bhargav Kumar Murala

Physical AI Engineer

Hyderabad · India

Now at · Capgemini

Sai Manasa Leburi

Autonomous Systems Engineer

New York · United States

Now at · NTT Data

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. This course uses the simulation and lab arrangements 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.

Is Python enough?+

Python is useful, but the core includes C++ because interfaces, ownership and runtime behavior are important parts of the intended work.

Which ROS version will I use?+

The teaching stack will be pinned as a complete compatible environment. Jazzy is the candidate baseline; newer releases are evaluated before migration.

Will I need to buy a robot?+

The core assessment uses simulation and recorded data. Any hardware extension and its access model will be stated separately.

Will I master navigation and manipulation equally?+

Mobile navigation is the depth route and capstone. Manipulation is a bounded practical introduction.

Does this prepare me for the other robotics courses?+

It provides relevant ROS 2 and integration foundations. Drones, humanoids and autonomous driving each add their own mathematics, controls and domain prerequisites.

How is the learning workload structured?+

The program combines live mentor-led classes, guided labs, project work and support sessions. An advisor can explain the current class format before enrollment.

Can I study online or on campus?+

RoboEdify offers its Hyderabad campus, live online classes and a working-professional format. Course-specific compute and any physical-lab access are explained separately.

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 recordings of every live session support your learning. Recordings remain available for the lifetime of your account.

Is placement guaranteed?+

No. Support includes portfolio/profile preparation, interview practice and role-fit introductions where available. RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.

What happens if I miss a practical requirement?+

Feedback identifies the missing capability and reassessment work. Attendance alone does not meet the completion standard. Confirm the course-specific reassessment arrangements before enrollment.

How do I learn about fees and lab requirements?+

Ask a course advisor about preparation, the current offering, fees, computer or equipment access and support terms before enrollment.

Still have a question?

Find your starting point in Robotics Software Engineer.
One million AI-native professionals by 2027.

Tell us about your programming, mathematics and engineering background. We’ll help you understand the preparation you need and the practical work this course is designed to develop.

Plan your learning

Course: Robotics Software Engineer
Preparation: Diagnostic-based preparation before the common core
Level: Specialist
Curriculum: 12 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.

Robotics Software Engineer

What does this engineering pathway involve?

Learn from behavior you can inspect and failures you can reproduce.

Learn through classes, labs and individual feedback.

Match the starting point to your existing skills.

Build skills you can demonstrate.

What you practise before the core.

Twelve modules, from foundations to an independent engineering assessment.

Engineering workflow and robotics foundations

ROS 2 communication and execution

Robot descriptions, transforms and kinematics

Simulation and controller interfaces

Sensors, calibration and replay

State estimation and localization

Mapping and Nav2 integration

Behavior trees and task recovery

Manipulation and motion-planning introduction

Integration testing and deployment discipline

Independent mobile-robot capstone

Technical interviews and portfolio defense

Use a focused, compatible engineering stack.

Three guided projects and an independent capstone.

Observable ROS 2 sensor and action system

Mapping and navigation investigation

Bounded manipulation scene

Indoor inspection robot with observable recovery

Demonstrate what you can do.

Prepare for relevant engineering work with evidence you can explain.

Build your portfolio. Prepare your profile. Practise your interviews.

Evidence from your own work.

Profile and resume preparation.

Interview practice and introductions.

Meet the team behind RoboEdify.

What employers say about RoboEdify’s AI and robotics graduates.

Meet alumni featured in our AI programs.

Come chat with us—on campus or online.

Support when you need to catch up.

Questions about prerequisites, tools and completion.

Find your starting point in Robotics Software Engineer.One million AI-native professionals by 2027.

Plan your learning

Find your starting point in Robotics Software Engineer.
One million AI-native professionals by 2027.