Speaker: Junjie Tang @ AWS Amarathon 2025
Summary by Amazon Nova
Guidance for AI-Driven Robotics
- Overview of objectives and benefits: integrate
Scalable Robotic
1: NVIDIA Isaac Sim for physics-based
2: Amazon EC2/EKS & Amazon Batch for scalable, parallel execution
3: Amazon Bedrock foundation models, and agents via MCP server for AI
4: Hugging Face LeRobot (LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry to robotics.)
5: Outcome: parallel simulations
Cloud-native pipeline combining NVIDIA Isaac Sim, Amazon compute, Bedrock models, MCP agents
Significance and Impact of
Faster training Scalable fleets Real-time reasoning Continuous
Drastically reduces
Enables parallel
Supports real-time
Continuous
Outcome: iterative
Target Industries for
Where simulation-driven training delivers safer, faster, tailored
Manufacturing Automation: Safer Commissioning, Reduced
Warehouse & Logistics, Robotics
Retail & Delivery: Efficient
Healthcare Assistive Robotics: Safer Patient
Agricultural & Environmental Robotics
Delivery Agent from
Amazon Professional Services
A comprehensive agent system across the consulting cycle
Enterprise-Grade Quality and Security
Multiple validation layers mitigate AI hallucinations
Secure, customer-controlled environments
Human oversight at strategic checkpoints
Comprehensive security controls and protocols
AWS Professional Services (ProServe) agents
A multi-agent AI system architecture, for software development and delivery, associated with AWS Professional Services (ProServe) agents. The agents interact to create and manage software solutions.
Sales Agent: The starting point, which initiates the process by feeding requirements or information into the workflow.
Delivery Agent: The central orchestrator that analyzes requirements, builds AI applications directly, and coordinates specialized work by delegating tasks to other agents.
Project Artifacts: An output generated from the initial input, likely documentation or initial plans, used by the Design Agent.
Design Agent: Takes "Project Artifacts" and produces a "Spec Package". It can also provide "Feedback" back to the Delivery Agent or the "Project Artifacts" step.
Spec Package: The output from the Design Agent, containing specifications for the build process.
Build Agent: Uses the "Spec Package" (guided by "Autopilot", an internal mechanism) to generate "Coding Artifacts".
Coding Artifacts: The generated code or application components resulting from the Build Agent's work.
Custom agents on AWS Transform: A separate, connected process that integrates with the main flow.
Security Agent: A persistent layer of the architecture, monitoring or enforcing security policies throughout the process.
Amazon Cloud stage/dev: Represents AWS environments (staging and development) where the resulting artifacts are deployed or managed.
Coding Artifacts are sent to the "dev" environment.
The "stage" environment appears to be an output or endpoint for the "Custom agents" process.
The system uses intelligent agents to potentially automate and accelerate the software development lifecycle, improving efficiency and quality.
Team:
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