pxRobotLearning
pxRobotLearning is an end-to-end platform for developing, training, validating, and deploying Physical AI systems. It combines simulation-first development, data collection, imitation learning, reinforcement learning, and optimized inference into a single, coherent pipeline that scales from research to real-world deployment.
- Simulation-Centered Learning Pipeline
- Flexible Learning Algorithms
- Sim-to-Real Adaptation & Fine-Tuning
- Production-Ready Deployment & Multimodal Learning
What We Deliver
Isaac Sim–Based Simulation Pipeline
- High-fidelity simulation foundation
The learning pipeline is built on Isaac Sim, providing physically realistic environments for robotic interaction and data generation - Scalable training environments
Supports large-scale parallel simulation for efficient policy training and evaluation - Flexible scene and sensor configuration
Enables rapid setup of robot models, sensors, and task scenarios
Reinforcement Learning & Imitation Learning Algorithms
- Multiple RL algorithm options
Provides a selection of state-of-the-art reinforcement learning algorithms suitable for different robotic tasks - Imitation learning from demonstrations
Supports learning from expert data collected via teleoperation or scripted policies - Unified training framework
Allows RL and IL methods to be combined or switched seamlessly within the same pipeline
Multi-Physics Sim-to-Sim Transfer
- Cross-engine validation
Supports sim-to-sim transfer across PhysX, Newton, and MuJoCo to improve generalization - Physics-aware robustness testing
Exposes policies to varying dynamics, contacts, and constraints - Reduced simulator bias
Mitigates overfitting to a single physics engine
Sim-to-Real Fine-Tuning
- Progressive domain adaptation
Fine-tunes policies trained in simulation using real-world data - Bridging the reality gap
Addresses discrepancies in dynamics, sensing, and actuation - Safe and efficient deployment
Enables gradual transfer from simulation to physical robots
High-Performance Deployment with ONNX & TensorRT
- Standardized model export
Converts trained models to ONNX for framework-independent deployment - TensorRT-optimized inference
Achieves low-latency, high-throughput execution on edge and embedded GPUs - Production-ready runtime stack
Designed for stable and scalable robotic deployment
Vision–Language Models & Vision–Language–Action Learning
- Multimodal perception and reasoning
Integrates vision and language understanding for complex tasks - Vision–Language–Action (VLA) policies
Enables robots to map high-level instructions to low-level actions - Fine-tuning for robotic domains
Adapts pretrained multimodal models to real robotic environments and tasks
The Robot Learning Platform delivers an end-to-end workflow that connects simulation, learning, adaptation, and deployment into a unified system. By combining high-fidelity simulation, flexible learning algorithms, robust sim-to-real transfer, and production-ready deployment with advanced multimodal learning capabilities, the platform enables scalable and reliable development of intelligent robotic behaviors across a wide range of real-world applications.