SB
Spandana Bala
ML Engineer
Hyderabad · India
Now at · Infosys
Robotics & Autonomous Systems · Practical career preparation
Humanoid Robotics & Whole-Body Systems
Dynamics, Whole-Body Control & Robot Learning
Coordinate the whole robot—and explain the constraints behind its motion. Model a floating-base robot, reason about contacts and build a bounded whole-body task in simulation. Compare model-based control and a scoped learned skill while measuring tracking, contact behavior, actuator limits and failure recovery.
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
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer
What does this engineering pathway involve?
Whole-body engineering coordinates a robot’s base, limbs and contacts to achieve a task within physical constraints. Humanoids add balance, changing contact and many interacting joints. This advanced course concentrates on modeling, control and measured evaluation of a bounded skill, rather than promising general-purpose humanoid autonomy.
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.
This is an advanced pathway. Graduates normally arrive after robotics-software and control foundations or equivalent project experience. Working engineers receive a diagnostic across dynamics, optimization, software and learning. A preparation bridge addresses bounded gaps; it does not replace an undergraduate controls or robotics foundation.
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.
Validate a humanoid modelInspect mass, inertia, frames, joint and contact assumptions.
Reason about whole-body dynamicsExplain floating-base state, Jacobians and support constraints.
Implement a bounded controllerCoordinate tasks while respecting joint, torque and contact limits.
Evaluate a learned skillUse a fixed baseline, separated evaluation and repeatable metrics.
Investigate transfer gapsTest latency, friction, actuation and sensor mismatch.
Defend a constrained demonstrationExplain failures, stop conditions and what has not been established.
Preparation areas
What you practise before the core.
Robotics softwareROS 2 or equivalent integration, reproducible simulation and numerical debugging.
Mathematics and optimizationLinear algebra, derivatives, constrained optimization and coordinate transforms.
Kinematics, dynamics and controlJacobians, rigid-body motion, feedback and actuator limits.
Machine-learning readinessPython, tensors, train/evaluation separation and basic reinforcement-learning concepts.
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.
01
Humanoid architecture and model validation
Engineering core
+
01
Humanoid architecture and model validation
Engineering coreTopics
•Floating base
•Joint topology
•Mass/inertia
•URDF/MJCF concepts
•Collision geometry
•Actuator assumptions
•Model provenance
Hands-on lab
Inspect one supplied humanoid model and compare mass, joint axes and contact geometry with its documented specification.
Failure scenario you solve
An incorrect inertial parameter makes the simulation appear stable for the wrong reason.
You build
Model manifest and validation notebook.
Assessed outcome: Identify a model inconsistency and explain its effect on downstream control.
02
Kinematics, Jacobians and task spaces
Engineering core
+
02
Kinematics, Jacobians and task spaces
Engineering coreTopics
•Frames
•Forward/inverse kinematics
•Jacobians
•Singularity
•Joint limits
•Task-space errors
•Numerical checks
Hands-on lab
Implement or validate a Jacobian and solve a bounded reach task with explicit limits.
Failure scenario you solve
A solver produces a mathematically valid pose outside the allowed joint range.
You build
Kinematics tests and constrained-reach analysis.
Assessed outcome: Compare analytical and numerical results and explain a failed target.
03
Floating-base dynamics and contact
Engineering core
+
03
Floating-base dynamics and contact
Engineering coreTopics
•Equations of motion
•Underactuation
•Support contacts
•Friction
•Contact Jacobians
•Center of mass
•Momentum concepts
Hands-on lab
Run a fixed-contact balance model and inspect forces and support assumptions.
Failure scenario you solve
A controller demands a tangential contact force beyond the available friction.
You build
Dynamics notebook and contact-feasibility plots.
Assessed outcome: Explain why a desired acceleration may be infeasible.
04
Actuation and low-level control
Engineering core
+
04
Actuation and low-level control
Engineering coreTopics
•Position/velocity/torque interfaces
•PD/impedance concepts
•Saturation
•Delay
•Damping
•Controller rate
•Observation filters
Hands-on lab
Compare bounded gains and delay in simulation while logging torque, error and oscillation.
Failure scenario you solve
A controller tracks well without limits but saturates when realistic actuator bounds are enabled.
You build
Controller response and limit-violation report.
Assessed outcome: Separate nominal tracking from physically achievable response.
05
State and contact estimation
Engineering core
+
05
State and contact estimation
Engineering coreTopics
•Floating-base pose/velocity
•IMU/encoder roles
•Contact state
•Uncertainty
•Delay
•Estimator/controller interface
Hands-on lab
Perturb a supplied state/contact estimate and observe the controller response.
Failure scenario you solve
A false contact assumption causes the controller to allocate force through an unsupported foot.
You build
Estimator interface and perturbation tests.
Assessed outcome: Identify the assumptions that must hold for the downstream controller.
06
Whole-body control and task prioritization
Engineering core
+
06
Whole-body control and task prioritization
Engineering coreTopics
•QP/hierarchical control concepts
•Posture and end-effector tasks
•Joint/torque/contact constraints
•Regularization
•Infeasibility handling
Hands-on lab
Complete a supplied constrained-control scaffold for a fixed-contact reach while maintaining posture.
Failure scenario you solve
Two requested tasks conflict and an infeasible solve produces no meaningful fallback.
You build
Controller configuration/code and task-priority analysis.
Assessed outcome: Explain active constraints and demonstrate a defined response to infeasibility.
07
Locomotion and contact-transition foundations
Engineering core
+
07
Locomotion and contact-transition foundations
Engineering coreTopics
•Support phases
•Gait and foothold concepts
•Trajectory tracking
•Center-of-mass planning
•Transition sensitivity
Hands-on lab
Evaluate a supplied locomotion baseline under bounded changes in speed and terrain.
Failure scenario you solve
A contact transition creates a tracking spike that is hidden in an average score.
You build
Phase-level tracking and failure report.
Assessed outcome: Explain which contact-transition behavior limits the evaluated skill.
08
Robot learning with a supplied baseline
Engineering core
+
08
Robot learning with a supplied baseline
Engineering coreTopics
•RL/ imitation roles
•Observation/action spaces
•Rewards
•Resets
•Training versus evaluation
•Seed variation
•Checkpoint selection
Hands-on lab
Train or adapt one small scoped skill from a qualified baseline and evaluate held-out perturbations.
Failure scenario you solve
A policy exploits the reward or simulator termination rule instead of completing the intended task.
You build
Training configuration, checkpoint provenance and evaluation report.
Assessed outcome: Demonstrate task success using independent metrics rather than reward alone.
09
Upper-body tasks and behavior composition
Engineering core
+
09
Upper-body tasks and behavior composition
Engineering coreTopics
•Perception-to-target interface
•Constrained reaching
•Object/contact assumptions
•Task sequencing
•VLA context
•Low-level control boundary
Hands-on lab
Connect a bounded target request to the whole-body reach controller while validating reachability.
Failure scenario you solve
A high-level request commands a target that violates balance or joint constraints.
You build
Task interface and rejection/recovery behavior.
Assessed outcome: Keep high-level requests inside the validated controller envelope.
10
Transfer analysis and supervised deployment literacy
Engineering core
+
10
Transfer analysis and supervised deployment literacy
Engineering coreTopics
•Friction/mass/delay randomization
•Actuator modeling
•Sensor mismatch
•Command limits
•External stop
•Physical access conditions
Hands-on lab
Build a perturbation matrix and a proposed supervised bench test of one transfer assumption.
Failure scenario you solve
A successful simulated policy fails when latency or friction changes slightly.
You build
Transfer-gap report and bounded hardware-test proposal.
Assessed outcome: State what simulation evidence does and does not support; no autonomous hardware release is inferred.
11
Independent whole-body systems capstone
Capstone
+
11
Independent whole-body systems capstone
CapstoneTopics
•Fixed model/task
•Constrained-control baseline
•Held-out perturbations
•Task/contact metrics
•Recovery
•Reproducible artifacts
Hands-on lab
Deliver a fixed-contact whole-body reaching/posture task and compare it under model and sensor perturbations.
Failure scenario you solve
The hand reaches the target only by violating a torque or contact constraint.
You build
Controller repository, trial traces, constraint report and demonstration.
Assessed outcome: Meet the frozen task contract, record violations and defend limitations across unseen cases.
12
Technical interviews and portfolio defense
Career preparation
+
12
Technical interviews and portfolio defense
Career preparationTopics
•Dynamics reasoning
•Controller trace reading
•Learning/evaluation limits
•Reproducibility
•Contribution evidence
Hands-on lab
Diagnose an unseen model, contact or control defect and explain the capstone.
Failure scenario you solve
A learner reports a success rate without defining failures or excluded trials.
You build
Project summary and individual technical defense.
Assessed outcome: Support each outcome claim with a metric, trial record and scope statement.
Scope note: This is advanced whole-body systems preparation. General-purpose humanoid intelligence, dexterous-hand mastery, full locomotion-stack authorship, all-terrain transfer and research publication outcomes are not promised. High-level language/VLA systems are context; they do not replace low-level constraint enforcement.
Tools and methods
Use a focused, compatible engineering stack.
Python/C++ and numerical toolsImplement model checks, controllers and analysis.
MuJoCo or a qualified dynamics backendStudy contact-aware model/control behavior.
Isaac Lab and a compatible backendRun the selected learned-skill exercise after qualification.
Supplied humanoid modelKeep one defined embodiment throughout the assessed task.
Logs, plots and version controlRecord constraints, failures and reproducible evaluations.
The core is simulation-based. A qualified small control model and supplied training baseline determine the compute requirements. Isaac Lab versions/backends and GPU access are specified only after qualification. Full-size humanoid ownership or unrestricted remote hardware access is not assumed.
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
Humanoid model and kinematics audit
Validate joint frames, numerical Jacobians and constrained reachability for one supplied model.
Portfolio evidence
Model manifestKinematics testsLimit checks
Guided project 2
Constrained whole-body reach
Complete a controller scaffold and inspect conflicts between posture, end-effector and contact requirements.
Portfolio evidence
ControllerConstraint plotsInfeasibility tests
Guided project 3
Scoped learned-skill evaluation
Adapt a supplied baseline and evaluate independent task metrics under held-out perturbations.
Portfolio evidence
Training recordCheckpointEvaluation report
Capstone
Contact-aware whole-body reaching and posture control
Use one supplied humanoid model for a fixed-contact upper-body reach task. Maintain the declared posture/contact constraints, handle infeasible targets and report repeated trials under unseen disturbances. Locomotion learning is a separate guided exercise, so the capstone remains feasible and assessable.
What you submit
- •A frozen scope and acceptance checklist.
- •Model checks, controller traces, constraint analysis and learned-skill 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
Humanoid Robotics & Whole-Body Systems
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
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 Controls / Simulation TraineeBuild model, constraint and evaluation evidence.
Junior Whole-Body Integration EngineerPractise task/control interface work subject to prior foundations.
Robot-Learning Evaluation TraineeDevelop reproducible baseline and perturbation testing skills.
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 model checks, controller traces, constraint analysis and learned-skill 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.
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 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. 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.
Can I join as a complete robotics beginner?
This advanced pathway requires robotics software, mathematics and control foundations. Begin with those prerequisites if your diagnostic shows broader gaps.
Will I work on a real humanoid?
The core assessment is in simulation. Any physical extension depends on a separately specified platform, supervision and access arrangement.
Will I train a policy from scratch?
You adapt a supplied, qualified baseline for one bounded skill. Large-scale foundation-model training is outside the core.
Will the capstone include walking and manipulation together?
The assessed capstone is fixed-contact whole-body reaching/posture control. A locomotion/learning baseline is a separate guided exercise.
Does a high simulation reward prove a deployable skill?
No. You evaluate independent task metrics, constraints and failure cases, then document transfer limitations.
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 Humanoid Robotics & Whole-Body Systems.
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
- Humanoid Robotics & Whole-Body Systems
- 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.