Physical AI and Robotics Foundation Models Corporate Training
Turn foundation models into your team's physical AI advantage with hands-on, instructor-led training.
Give your enterprise teams the physical AI skills that turn foundation models into working robots. Edstellar's Physical AI & Robotics Foundation Models corporate training gives your engineers practical techniques to adapt vision-language-action models, train in simulation, and deploy safe control stacks. Live trainers deliver each session tailored to your robotics goals, roles, and maturity, with measurable outcomes for leadership.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build a physical-AI-ready team
THE EDSTELLAR PHYSICAL AI CAPABILITY MODEL
Assess physical AI skills
Skill checks reveal where your team stands with foundation models and robotics today.
One physical AI standard
Set shared patterns for simulation, model adaptation, and robot control.
Practice in hands-on labs
Train and deploy robot policies in guided labs with instructor coaching.
Prove the lift
Re-run skill checks and task reviews so leadership sees capability rise.
Scale across teams
Roll out one physical AI standard across teams, batches, and locations.
Need more than one program?Get a group quote
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Program Overview: What is Physical AI and Robotics Foundation Models Corporate Training?
Physical AI & Robotics Foundation Models is Edstellar's instructor-led technical program for robotics and AI teams building robot capability on foundation models. Participants cover vision-language-action models, simulation and robotics data, robot control and policy execution, and evaluation and safety. The focus stays on robots that work in the physical world.
Across 20 hours, participants work with vision-language-action model concepts, build simulation scenarios for robotics data, and practice policy execution on realistic control tasks with instructor guidance. Evaluation and safety modules address what it takes to trust a learned policy on hardware. The program closes with a robot foundation build capstone.
Robotics teams stall when foundation models stay in papers and sim demos never reach hardware: policies that work in simulation fail on contact, and safety concerns stop deployment. This program teaches teams to close the loop from model to simulation to real control, with evaluation that earns hardware time.
The closing build is a robot foundation exercise the team owns end to end: taking a foundation-model-driven policy through simulation, evaluation, and a deployment plan for real hardware. Building it working is what makes physical AI real, because the gap between simulation and contact is where robotics programs live or die.
Teams pilot robot prototypes fast, but without shared physical AI patterns, simulation runs stall, sim-to-real transfers fail, and unsafe behaviors reach hardware. This program builds one clear standard: train in simulation, adapt foundation models deliberately, and deploy robot control with safety from day one.
The result: engineers who ship reliable robot capability with foundation models, from simulation to safe deployment, supporting faster delivery and safer automation across your organization.
What's Included in this Workshop for Your Enterprise Teams
- 1
Physical AI foundations
Core foundation model and robotics ideas.
- 2
Hands-on robotics labs
Simulate and control robots with coaching.
- 3
Simulation toolkits
Reusable patterns for training robots in sim.
- 4
Expert trainers
Learn from practitioners in physical AI.
- 5
Capstone robot build
Ship a robot task pipeline for a use case.
- Training delivered at any of your offices globally
- 10+ languages
- Schedule around your calendar
- Group size set along with you
- Run as one cohort or split into batches
- Instructor-led (virtual/onsite/hybrid)
- Recurring and annual options
- Trainer sourcing, matching and logistics
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
- Plan from 2000+ Industry-ready Training Programs
- Hands-On Labs on Live Infrastructure, Led by Industry Experts
Physical AI and Robotics Foundation Models Course Topics and Program Outline
A six-module technical program on physical AI: foundation model concepts, vision-language-action models, simulation, robot control, and safe deployment. Five guided hands-on labs plus a capstone robot build.
Built by Edstellar with robotics AI trainers
Built by practitioners in physical AI and robotics, with lab feedback on every exercise your team completes.
Baseline each participant's physical AI readiness before module 01 begins
Build strong physical AI foundations in instructor-led sessions for your team
Reinforce skills through guided hands-on labs on robotics foundation models
Apply physical AI skills to real-world scenarios and a capstone robotics project
A short pre-training assessment sets each participant's entry level, so the program starts where your team actually is. The rest is weighted toward hands-on practice: guided labs on robot simulation and foundation-model control, plus realistic scenarios drawn from your team's work, not slides.
Delivered as
Entry level set by the assessment. Depth and schedule customized to your team.
a)Foundation models for robotics overview
b)Robot sensing and perception basics
c)Simulation environments overview
d)Baseline skills check
a)Architecture of VLA models
b)Vision encoders and language grounding
c)Action prediction and control outputs
d)Inference lab: run a VLA task
a)Building simulated training scenes
b)Generating synthetic robot data
c)Sim-to-real transfer principles
d)Simulation lab: collect training data
a)Motion planning basics
b)Policy deployment patterns
c)Safety constraints and fallbacks
d)Control lab: execute a policy
a)Success metrics for robot tasks
b)Failure analysis and guardrails
c)Deploying models to robots
d)Evaluation lab: score a robot task
a)Integrate skills across modules
b)Build a robot task pipeline
c)Present the pipeline for review
d)Leave with a deployment plan
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
By the end of this program, your engineers will adapt foundation models for robotics, train in simulation, and deploy safe control stacks, covering VLA models, sim-to-real transfer, and evaluation.
Describe physical AI concepts
Explain how foundation models give robots perception and control.
Evaluate VLA models
Compare vision-language-action models for robot task suitability.
Build simulation scenes
Create simulated environments and datasets for robot training.
Run sim-to-real transfer
Apply transfer techniques that move policies from sim to robots.
Deploy robot control stacks
Deploy models with safety checks for robot control.
Evaluate robot task safety
Score robot behavior and apply guardrails for safe operation.
Ship robot applications
Connect models, control, and safety into working robot apps.
Teams that Benefit from this Physical AI Group Training
This program is built for engineers and technical teams building robot capability: robotics engineers, applied AI engineers, data scientists, and platform teams. Content adapts to role and experience, so every cohort practices on the simulation, models, and robot tasks they actually own.
Ship reliable robot capability faster.
Robotics teams learn to adapt foundation models to robot tasks, train in simulation, and deploy control stacks with shared safety patterns.
Workflow friction
- Sim-to-real transfers fail
- No shared control patterns
- Unsafe behavior on hardware
How the program helps
- Train models in simulation
- Deploy with shared patterns
- Keep robots safe
Practice use cases
- Simulation labs
- Control builds
- Safety drills
Put foundation models on robots.
Applied AI engineers learn to evaluate and adapt vision-language-action models for real robot tasks, with disciplined evaluation and transfer.
Workflow friction
- Model choice is guesswork
- Policies fail in the real world
- No evaluation standard
How the program helps
- Choose VLA models well
- Transfer policies reliably
- Evaluate tasks consistently
Practice use cases
- Model labs
- Transfer builds
- Eval reviews
Build robot training data.
Data science teams learn to generate synthetic robot datasets and measure task performance, keeping training data rich and representative.
Workflow friction
- Robot data is scarce
- Synthetic data feels fake
- No task metrics defined
How the program helps
- Generate rich robot data
- Validate simulation data
- Define task metrics
Practice use cases
- Data labs
- Dataset builds
- Metric reviews
Run robot workloads at scale.
Platform teams learn to provision simulation compute, manage model versions, and govern robot deployments so engineering teams ship safely.
Workflow friction
- Simulation compute is scarce
- Model versions drift apart
- Deployment governance gaps
How the program helps
- Provision simulation compute
- Version models clearly
- Govern robot deployments
Practice use cases
- Compute labs
- Version reviews
- Policy drills
Each cohort works through role-relevant physical AI labs, then leaves with reusable patterns for day-to-day work.
Prerequisites: This program benefits robotics engineers, ML practitioners, and researchers exploring physical AI and robot foundation models. Familiarity with Python, machine learning, or control basics helps but is not required, and no prior robotics or foundation-model experience is needed. Content adapts to each cohort's platforms, tools, and experience level.
From foundation-model concepts to deploying a robot system
The learn modules cover physical AI foundations and vision-language-action models, so every team can judge what foundation models can do for robot tasks. Practice modules build the engineering loop: generating synthetic training data in simulation, executing control policies, and running evaluation, safety, and deployment discipline. The capstone assembles a complete robot foundation-model build, trained in simulation and governed for real hardware.
Teams move from understanding physical AI foundations to training, evaluating, and safely deploying foundation models on real robots.
Different Teams, Locations, Time Zones, or Languages? We've Got You Covered.
Whether your teams are spread across countries, time zones, languages, or experience levels, we deliver tailored training programs that ensure a consistent learning experience across your global workforce.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Onsite, Virtual and Hybrid
Every mode is instructor-led and tailored to your robotics stack before delivery. The curriculum, hands-on labs, assessments, and capstone stay the same across onsite, live virtual, and hybrid formats, so capability is consistent however you run it.
Onsite Training
Your instructor delivers at your facility with labs set up for your team. Every engineer simulates and controls robots with coaching. Best for co-located teams wanting maximum interaction.
- Instructor delivers at your facility
- Labs built around your robotics use cases
- Build and control robots together with coaching
- Best for co-located teams wanting maximum interaction
Live Virtual Training
Live virtual sessions run instructor-led on your preferred platform. Your team joins physical AI labs, breakout exercises, and real-time code reviews with the instructor. Ideal for distributed teams needing hands-on practice without travel.
- Live instructor-led sessions on your platform
- Guided labs with breakout troubleshooting
- Screen-shared code reviews in real time
- Ideal for distributed teams across locations
Hybrid Training
Live instruction plus supervised hands-on labs, across onsite and virtual sessions. Your team simulates, adapts, and deploys robot models with instructor coaching. Best for engineering teams with flexible schedules and real build work.
- Live instruction across onsite and virtual sessions
- Guided physical AI labs with instructor coaching on your roles
- Simulate and control robots in real time
- Best for flexible schedules with real hands-on practice
Need a customized training format that works for your team?
Our training delivery is customized to your team's schedule, full days, half days, sessions spread across weeks, or modular formats. Share your preferred timeline, and we'll plan the training around it.
Frequently Asked Questions
Share team size, preferred dates, and delivery mode in the lead form, or book a short discovery call first. We reply with a tailored proposal covering scope, schedule, and pricing for your group.
Yes. Book a short discovery call and we will map your training needs, robotics maturity, and timelines before sharing a plan.
Yes. Live virtual sessions run instructor-led on your preferred platform, with hands-on physical AI labs, breakout exercises, and real-time feedback.
Yes. We deliver onsite at your offices, with the agenda and labs adapted to your team's robotics setup, use cases, and workflows.
The standard program runs over two full days. Duration can be adjusted to fit your schedule, from focused one-day sessions to extended multi-day formats.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
Physical AI builds on ROS 2 and reinforcement learning. Your team needs robotics software skills and learning from rewards first, because foundation models for robots combine both; the results then go into field operations with humanoid and mobile robots.
ROS 2 FIRST
ROS 2 & Robotics Software Engineering Training
Build robotics software in ROS 2, the framework most robot learning work runs inside.
REINFORCEMENT LEARNING
Reinforcement Learning & Agent Optimisation Training
Train agents with rewards and policies, a core method behind robot learning.
ROBOTICS FOUNDATION MODELS
Physical AI & Robotics Foundation Models Training
Use robotics foundation models and vision-language-action models to give robots general skills.
RUN IN THE FIELD
Humanoid & Mobile Robotics Operations Training
Run AI-driven humanoid and mobile robots in real operations with monitoring and safety.
Why Choose Edstellar as Your Physical AI and Robotics Foundation Models Corporate Training Provider
As your training partner, we make comparing providers simple: share your training needs and we shape a Physical AI & Robotics Foundation Models program around your stack, timelines, and delivery mode. You get a matched trainer profile, deep robotics expertise, and a plan your leaders can approve with confidence.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Content tailored to your physical AI and robotics needs and mapped to your team's real physical AI and robotics work.
Proven Methodology
5+ years delivering corporate training to robotics and applied AI engineers across industries and regions.
Practice-Based Learning
5 hands-on labs across 6 modules. Your team builds robotics foundation-model pipelines with instructor guidance.
Expert Trainers
5,000+ certified trainers matched to your physical AI and robotics, with a full trainer profile shared before your program is booked.
Global Scale
One physical AI and robotics program across your offices worldwide, in hybrid, onsite, or live virtual.
Measurable Outcomes
Baseline-to-capstone measurement and Skills Matrix reporting keep your physical AI and robotics capability lift visible.
Connect Robotics Foundation Models With Controlled Task Execution and Safety Evaluation
Edstellar guides robotics and AI teams from foundation-model concepts to an integrated robot task pipeline. Participants explore vision-language-action inference, simulated data, and policy execution while examining constraints and failure cases. The capstone connects these components, with review focused on task evidence, safety assumptions, and the limits of proposed deployment plans.
- Step 01
Consult
We review Python and machine-learning readiness alongside familiarity with sensing, coordinate frames, and feedback to identify gaps before vision-language-action and policy exercises.
- Step 02
Plan
We establish physical AI foundations before VLA inference, simulation data generation, and control policies, then integrate task evaluation, failure analysis, and deployment planning.
- Step 03
Align
We align robotics and model-evaluation expertise with the cohort's programming experience, confirming suitable simulation environments and explicit safety boundaries for control exercises.
- Step 04
Customize
We adapt simulated scenes and robot task briefs to relevant sensing and manipulation challenges, keeping success criteria, control constraints, and fallback assumptions visible.
- Step 05
Execute
Participants run VLA tasks, collect simulated data, and examine policy behavior, receiving feedback on language grounding, task failures, and the suitability of guardrails.
- Step 06
Evaluate
We review the robot task pipeline and deployment plan for demonstrated task performance, explained failure cases, explicit safety constraints, and realistic sim-to-real limitations.
Also from Edstellar
Talent assessments
Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
Physical AI & Robotics Foundation Models Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request a Quote for Physical AI Training for Your Team
Tell us your team size, engineering roles, and preferred delivery mode. We will confirm availability and respond with a tailored training plan covering scope, schedule, and pricing.