Robotics Systems

Robots that learn,
adapt, and act
in the real world.

ALT designs, simulates, and builds custom robotic systems — from concept and URDF modeling through NVIDIA Isaac Sim training, ROS2 integration, reinforcement learning policy development, and physical deployment. Full-stack robotics engineering for Physical AI applications.

Start a robotics project Physical AI overview

ROS2

Robot Operating System 2 — Nav2, ros2_control, MoveIt2, Isaac ROS packages

Isaac Sim

NVIDIA Omniverse physics-accurate simulation for digital twins and synthetic training data

Isaac Lab

GPU-parallelized reinforcement learning framework — multi-robot policy training at scale

Edge AI

On-device inference on Jetson, FPGA, and custom embedded hardware — no cloud dependency

ALT AM

In-house additive manufacturing for custom robot structures in CF composites, PEEK, and novel materials

Our differentiator

Most robotics integrators work in software or hardware. ALT does both — simulating robot behavior in Isaac Sim, training policies with reinforcement learning, deploying on ROS2, and fabricating the physical hardware in-house. One team closes the entire loop from simulation to physical deployment.

Technology stack

The ALT robotics engineering stack.

ALT's robotics capability is built on the NVIDIA Isaac ecosystem and ROS2 — the open-standard platforms used by the world's leading robotics research groups and commercial deployments — augmented by ALT's own additive manufacturing and edge AI capabilities to close the sim-to-real gap.

Middleware

ROS2 — Robot Operating System 2

The open-standard robotics middleware that ALT uses as the communication backbone for all robot systems. ROS2's DDS-based architecture provides real-time messaging, hardware abstraction, and a mature ecosystem of packages for navigation, manipulation, perception, and control.

Nav2 — autonomous navigation and path planning for mobile robots
MoveIt2 — motion planning for robotic arms and manipulators
ros2_control — real-time hardware interface abstraction layer
Isaac ROS — NVIDIA-accelerated ROS2 packages: Visual SLAM, nvBlox, AprilTag, FoundationPose
Custom URDF and MJCF robot description files for any robot morphology
Custom ROS2 message types and hardware drivers for novel sensor configurations

Nav2MoveIt2ros2_controlIsaac ROS

Simulation

NVIDIA Isaac Sim — physics-accurate digital twins

Isaac Sim is ALT's primary simulation environment — built on NVIDIA Omniverse and OpenUSD, the same physics engine used in aerospace and automotive simulation. Robots trained in Isaac Sim experience gravity, sensor noise, surface friction, contact dynamics, and lighting conditions that transfer to real hardware with minimal sim-to-real gap.

Photorealistic, physics-accurate simulation for sensor validation and policy training
Realistic sensor models: camera, LiDAR, IMU, depth, tactile — matching real hardware
Synthetic training data generation via Omniverse Replicator — domain randomization at scale
Digital twin creation from CAD models or 3D scans of real environments
Multi-robot fleet simulation for coordination policy development
URDF/MJCF import — any robot morphology simulated directly from design files

Digital twinSensor simulationDomain randomization

Robot learning

Isaac Lab — GPU-parallelized reinforcement learning

Isaac Lab is the open-source reinforcement learning framework built on Isaac Sim — optimized for training robot policies at scale using thousands of parallel simulation instances on GPU. ALT uses Isaac Lab to train locomotion, manipulation, and navigation policies that transfer directly to physical robot hardware.

GPU-parallelized RL — thousands of simulated robots training simultaneously
Reinforcement learning: PPO, SAC, TD3, TRPO — reward shaping for physical constraints
Imitation learning and demonstration-guided policy initialization
Curriculum learning — progressively harder environments for robust policy training
Sim-to-real transfer with domain randomization to bridge the reality gap
Policy distillation — compressing complex policies for deployment on edge hardware

PPO / SACSim-to-realCurriculum learningPolicy distillation

Simulation to deployment

From Isaac Sim training
to real-world deployment.

The sim-to-real pipeline is where most robotics projects lose time and money. ALT's integrated workflow — from URDF design through Isaac Sim training, ROS2 integration, and physical hardware deployment — is designed to minimize the gap between simulated and real-world robot performance.

Why Isaac Sim closes the sim-to-real gap better than alternatives

Traditional robotics simulation (Gazebo, PyBullet) uses simplified physics that creates a large gap between simulated and real behavior — policies trained in these environments often fail on real hardware. Isaac Sim's GPU-accelerated physics, sub-millimeter contact dynamics, and photorealistic sensor simulation produce trained policies that transfer to physical robots with significantly less real-world fine-tuning. For Physical AI applications where real-world failures are costly, this isn't an academic distinction — it's the difference between a deployable system and one that spends weeks in hardware debugging.

Stage 01

Robot modeling & URDF/MJCF

ALT's mechanical design team creates the robot structure in SolidWorks, exports to URDF or MJCF format, and imports into Isaac Sim — with accurate mass, inertia, joint limits, and collision geometry. For Physical AI robots, ALT designs the physical hardware and the simulation model together.

Stage 02

Environment design & domain randomization

We build the simulated environment in Isaac Sim — including terrain variation, lighting randomization, object placement randomization, and sensor noise injection. Domain randomization is the key technique that makes trained policies robust to real-world variation that the simulation never perfectly captures.

Stage 03

RL policy training in Isaac Lab

Thousands of parallel robot instances train simultaneously on GPU — each experiencing different randomized conditions. ALT designs reward functions that encode the physical constraints and performance objectives of your application: stability, efficiency, task completion, safety margins.

Stage 04

ROS2 deployment & hardware integration

Trained policies are exported and deployed via ROS2 on the target edge hardware — NVIDIA Jetson, custom FPGA, or embedded MCU. Isaac ROS packages provide the bridge between trained models and hardware drivers. ALT validates in simulation before touching real hardware, and iterates rapidly when gaps appear.

Reinforcement learning

RL-trained robot policies
for real-world deployment.

Reinforcement learning produces robot behaviors that adapt to variation, handle novel situations, and improve over time — capabilities that scripted automation and classical control cannot match. ALT designs and trains RL policies for locomotion, manipulation, navigation, and perception tasks across multiple robot morphologies.

Locomotion policy training

Legged robots — quadrupeds, bipeds, hexapods — learn stable, energy-efficient gait patterns through RL in Isaac Lab. Policies trained across thousands of terrain variations (slopes, steps, uneven ground, gaps) produce robust locomotion that handles real-world terrain variation without explicit programming. Reward functions encode stability, efficiency, and target velocity tracking.

QuadrupedBipedGait optimizationTerrain adaptation

Dexterous manipulation

Robot hands and grippers learn object manipulation — pick-and-place, in-hand rotation, compliant grasping of deformable objects — through RL in Isaac Sim with physics-accurate contact dynamics. Force sensor feedback enables tactile-guided manipulation policies that adapt grip strength and approach angle to object properties in real time.

GraspingIn-hand manipulationForce feedbackDeformable objects

Autonomous navigation

Mobile robots learn obstacle avoidance, goal-directed navigation, and dynamic environment response through RL — going beyond Nav2's geometric planner to handle crowded, dynamic, and sensor-degraded environments. Policies trained across thousands of randomized environments in Isaac Sim deploy directly to real hardware via ROS2 Nav2 integration.

Obstacle avoidanceNav2Dynamic environmentsSensor degradation

Multi-agent & swarm coordination

Multi-robot systems — drone swarms, warehouse robot fleets, collaborative manipulation — trained in Isaac Sim's multi-robot environment using multi-agent RL. Coordination policies emerge from individual reward signals without explicit programming of inter-robot protocols. Deployed via ROS2 multi-agent frameworks for real-world fleet operation.

Multi-agent RLDrone swarmsFleet coordinationEmergent behavior

PPO

Proximal Policy Optimization

Default for locomotion and navigation. Stable training, good sample efficiency for continuous control tasks.

SAC

Soft Actor-Critic

Preferred for manipulation. Maximum entropy framework encourages exploration and produces robust, generalizable policies.

TD3

Twin Delayed DDPG

High-precision continuous control. Preferred for tasks requiring fine motor control with low variance in execution.

IL / DAgger

Imitation Learning

Policy initialization from human demonstration — accelerates RL training by starting near the target behavior rather than from random.

Robot morphologies

Robot types ALT designs,
simulates, and builds.

ALT's robotics capability spans multiple robot morphologies — each requiring different control architectures, RL reward structures, and physical design approaches. The common thread is the Isaac Sim + ROS2 + edge AI stack that ALT applies across all of them.

Legged robots — quadrupeds & bipeds

Four-legged and two-legged robots that navigate terrain impossible for wheeled platforms. ALT trains locomotion policies in Isaac Lab across varied terrain — slopes, stairs, rubble, soft ground — producing gaits that adapt in real time to terrain variation through proprioceptive sensing.

SimulationIsaac Lab, IsaacGym

RL algorithmPPO with terrain curriculum

Controlros2_control, custom actuator interfaces

ApplicationsInspection, search & rescue, exploration

QuadrupedTerrain adaptationGait RL

Robotic arms & manipulators

6-DOF and higher serial manipulators for pick-and-place, assembly, inspection, and precision manufacturing. ALT integrates MoveIt2 for motion planning, custom end-of-arm tooling designed and printed in-house, and RL-trained grasping policies for deformable and irregular objects.

PlanningMoveIt2, cuRobo (CUDA-accelerated)

RL algorithmSAC for dexterous manipulation

SensingForce/torque, tactile, depth camera

ApplicationsAssembly, inspection, pick-and-place

MoveIt2Dexterous graspingcuRobo

Autonomous drones & UAVs

Fixed-wing, quadrotor, and VTOL UAVs trained in Isaac Sim for autonomous flight, sensor fusion navigation, and mission execution without remote pilots. ALT designs and prints drone airframes in PAHT-CF and PPA-CF in-house and deploys flight autonomy stacks via ROS2 on edge hardware.

AutonomyPX4 + ROS2 bridge, Nav2 for UAV

Sensor fusionGPS, IMU, optical flow, LiDAR, barometer

StructureIn-house PAHT-CF / PPA-CF frames

ApplicationsInspection, mapping, delivery, surveillance

PX4 + ROS2PAHT-CF framesSensor fusion

Mobile manipulators (AMR + arm)

Combined autonomous mobile robot platforms with integrated robotic arms — capable of navigating to a work location and performing manipulation tasks at the destination. ALT integrates Nav2 mobile navigation with MoveIt2 arm control in a unified ROS2 architecture with coordinated task planning.

NavigationNav2 with 3D costmaps

Arm controlMoveIt2 with whole-body planning

CoordinationBehaviorTree.CPP, ROS2 task planning

ApplicationsWarehouse, field service, logistics

AMRWhole-body planningBehaviorTree

Biomimetic & soft robots

Robots inspired by biological motion — fish, insects, snakes, and human musculature — using ALT's synthetic tissue materials and gradient-stiffness composites. Soft robot actuation policies trained in Isaac Sim's deformable body simulation, deployed via ROS2 on custom pneumatic and cable-driven hardware.

SimulationIsaac Sim deformable body + FEA

MaterialsALT synthetic tissues, gradient TPU

ActuationPneumatic, cable-driven, SMA

ApplicationsMedical, inspection, human interaction

BiomimeticSoft roboticsSynthetic tissues

Advanced prosthetics & wearable robots

Intelligent prosthetic limbs and powered exoskeletons that use EMG sensing, edge AI, and RL-trained control policies to restore natural function. ALT designs the physical structure, trains the control policy in Isaac Sim using human motion data, and deploys on embedded hardware inside the prosthetic device.

SensingEMG, IMU, force, tactile

ControlEdge inference on embedded MCU

StructurePEEK, CF composites, ALT tissues

ApplicationsUpper/lower limb prosthetics, exoskeleton

EMG controlProstheticsExoskeleton

Real-world applications

Where ALT robotics systems are deployed.

ALT's robotics capability serves applications where fixed automation fails — where variability, unstructured environments, and real-world unpredictability require systems that perceive, reason, and adapt.

Industrial inspection & maintenance robotics

Autonomous inspection robots trained in Isaac Sim to detect structural anomalies, surface defects, and equipment failures in industrial environments — power plants, oil & gas infrastructure, manufacturing facilities. Visual SLAM and nvBlox for 3D environment mapping deployed via Isaac ROS on Jetson edge hardware. Anomaly detection policies trained on synthetic data generated in Isaac Sim using Omniverse Replicator — eliminating the need for large real-world defect datasets before deployment.

Adaptive manufacturing robots

Industrial robots that go beyond scripted automation — detecting part variation in incoming materials, adjusting assembly force mid-cycle, and flagging anomalies before they become defects. Force-sensor-guided RL policies trained in Isaac Sim with physical contact dynamics, deployed via ros2_control on real manipulators. Reduces scrap rates and line stoppages on production lines handling high-variation incoming parts.

Autonomous drone operations

Autonomous UAV systems for mapping, inspection, delivery, and surveillance — trained in Isaac Sim for navigation in GPS-denied and dynamically obstructed environments. PX4 + ROS2 bridge for hardware abstraction across multiple drone platforms. ALT prints custom airframes in PAHT-CF in-house for minimum weight and maximum structural performance, and integrates edge AI on Jetson for on-board perception and decision-making without ground station connectivity.

Intelligent prosthetics & assistive devices

Upper and lower limb prosthetics with RL-trained control policies that learn from EMG surface signals to predict intended motion and execute it through motor-driven mechanisms. Policies trained in Isaac Sim using musculoskeletal models and deployed on embedded MCUs inside the prosthetic device. No cloud connection required — full inference on-device at the millisecond latency required for natural limb control.

Agricultural & field robotics

Mobile robots for precision agriculture — crop monitoring, targeted treatment, and selective harvesting in unstructured outdoor environments. Legged platforms trained in Isaac Lab for traversal of varied terrain (furrows, mud, slopes) that wheeled AMRs cannot navigate. Computer vision pipelines for crop status assessment deployed via Isaac ROS on edge hardware in the field.

Search, rescue & disaster response

Legged and aerial robots trained for operation in post-disaster environments — rubble fields, collapsed structures, flooded terrain — where human entry is dangerous and wheeled platforms are inoperable. Locomotion policies trained in Isaac Lab across randomized rubble terrain, with sensor fusion architectures that maintain navigation capability when individual sensors degrade in smoke, dust, and low-light conditions.

Why ALT for robotics systems.

Most robotics vendors do software integration or hardware fabrication. ALT does the full stack — from Isaac Sim training and ROS2 deployment through in-house design and fabrication of the physical robot hardware. No integration overhead between the software team and the hardware vendor.

Isaac Sim + ROS2 + in-house hardware — one team

ALT trains policies in Isaac Lab, deploys them via ROS2, and fabricates the physical robot hardware in-house. The same engineers who write the RL reward function design the robot's mechanical structure and the edge hardware it runs on. No handoffs between simulation, software, and hardware teams.

Additive manufacturing closes the hardware gap

Novel robot geometries — topology-optimized structures, embedded sensor channels, gradient-stiffness components, biomimetic forms — require additive manufacturing. ALT prints robot components in PAHT-CF, PPA-CF, PEEK, and proprietary novel materials in-house, enabling robot hardware that a conventional machining shop cannot produce.

Domain randomization for robust sim-to-real transfer

ALT's Isaac Sim environments include systematic domain randomization — lighting, texture, mass, friction, sensor noise, environmental variation — so trained policies are robust to the conditions the simulation never perfectly models. This is what makes the gap between Isaac Sim performance and real-world performance manageable rather than catastrophic.

Edge-native deployment — no cloud dependency

Every robot ALT builds runs its perception, planning, and control on embedded edge hardware — Jetson Orin, custom FPGA, or embedded MCU — without cloud connectivity. For field robots, medical devices, and defense applications, cloud dependency isn't an option. ALT designs for edge-first from the start.

Novel materials for novel robot morphologies

ALT's synthetic tissue composites, biomimetic ceramic structures, and gradient-stiffness materials enable robot designs that conventional engineering materials can't support. Soft robots, biomimetic actuators, and prosthetic interfaces all require materials that behave like biology — which ALT develops in-house and prints directly into robot structures.

Common questions.

What robotics teams, research institutions, and product developers ask most often about ALT's robotics systems capability.

What is ROS2 and why does ALT use it for robotics?

ROS2 (Robot Operating System 2) is the open-standard middleware used by the majority of advanced robotics research and commercial deployments globally. It provides real-time DDS-based communication between robot components, hardware abstraction layers, and a mature ecosystem of packages for navigation (Nav2), manipulation (MoveIt2), and perception (Isaac ROS). ALT uses ROS2 because it is the standard — systems built on ROS2 integrate with the widest range of sensors, actuators, and third-party components, and benefit from continuous development by the global robotics research community.

What is NVIDIA Isaac Sim and how does it improve sim-to-real transfer?

Isaac Sim is NVIDIA's physics-accurate robotics simulator, built on Omniverse. Unlike traditional simulators (Gazebo, PyBullet) that use simplified physics engines, Isaac Sim provides GPU-accelerated rigid body dynamics, sub-millimeter contact simulation, photorealistic rendering, and realistic sensor models (LiDAR, camera, IMU, depth) that closely match real hardware behavior. The result is a significantly smaller gap between simulated policy performance and real-world robot behavior — trained policies transfer to physical hardware with less real-world debugging and fine-tuning than alternative simulators.

What is reinforcement learning for robotics and how does ALT apply it?

Reinforcement learning (RL) is a training approach where a robot learns behavior by maximizing a reward signal through trial-and-error interaction with its environment. In Isaac Lab, thousands of simulated robot instances train simultaneously on GPU — each accumulating experience across millions of timesteps that would take months to collect in the real world. ALT designs the reward functions that encode your robot's performance objectives, selects the appropriate RL algorithm (PPO for locomotion, SAC for manipulation), and trains policies that transfer to real hardware via domain randomization.

What is domain randomization and why does it matter for sim-to-real transfer?

Domain randomization is the technique of varying simulation parameters — mass, friction, lighting, sensor noise, object textures, joint stiffness — across training episodes, so the trained policy learns to be robust to variation rather than overfitting to a single simulated configuration. When deployed on real hardware, the real world's variations fall within the distribution the policy was trained on — rather than appearing as out-of-distribution inputs that cause the policy to fail. ALT implements systematic domain randomization in every Isaac Sim training environment as a core part of the sim-to-real pipeline.

Can ALT design and fabricate the physical robot hardware as well as the software?

Yes — this is ALT's core differentiator. We design robot structures in SolidWorks, simulate them in Isaac Sim, train control policies in Isaac Lab, deploy via ROS2, and fabricate the physical hardware in-house using additive manufacturing in carbon fiber composites, PEEK, and ALT's proprietary novel materials. Embedded electronics integration and edge AI deployment on Jetson or custom embedded hardware are included in the full-stack engagement. You do not need a separate mechanical engineering vendor or a contract manufacturer — ALT does it all.

What edge hardware does ALT deploy robotics AI on?

ALT primarily deploys on NVIDIA Jetson Orin (for perception-heavy applications requiring GPU inference), custom FPGA platforms (for deterministic real-time control loops), and embedded ARM MCUs (for lightweight control tasks). Model selection and quantization ensure that trained RL policies and perception models meet the latency requirements of real-time robot control on the target hardware — typically sub-5ms for control loops and sub-30ms for perception pipelines.

Ready to build a robot
that actually works?

From Isaac Sim simulation and RL policy training through ROS2 deployment and physical hardware fabrication — tell ALT what your robot needs to do and we'll scope the full system.