Building AI systems for the physical world.
Jangara Bliss is a computer engineering student focused on machine learning that leaves the notebook — vision-language navigation deployed on a real humanoid, locomotion policies trained in simulation, and the systems engineering that connects models to machines. Graduate study is the next step: deeper AI/ML foundations for building serious physical-AI systems.
Robot camera stream
Booster K1
Relay / control machine
Model inference
RTX 5090 · 8B VLA
Velocity commands
robot SDK
Simulation
Isaac Sim / Isaac Lab
Schematic of the real three-machine deployment — full diagram pending in the project writeup.
Real humanoid deployment
8B vision-language-action (VLA) model running on a Booster K1
Multi-machine inference relay
RTX 5090 inference → relay/control node → robot SDK
Simulation & RL
Isaac Sim / Isaac Lab, PPO locomotion, benchmark evaluation
Robotics software
ROS, MoveIt, Gazebo, YOLOv8 perception pipelines
Hardware-adjacent
Embedded, FPGA, PCB — three faculty labs and a NASA-challenge hexapod
Builder orientation
EVA president & co-founder · Goldman Sachs Emerging Leaders · Katz Leadership Award
Featured projects
Case studies over screenshots — each entry is structured around evidence.
Lanes of work
Seven lanes, one through-line: models that have to survive real hardware.
Multimodal AI
Vision-language and vision-language-action models as interfaces between human intent and machine behavior.
Computer Vision & Perception
Detection and perception pipelines that hold up outside the dataset — YOLOv8 in practice, sensing under real constraints.
Reinforcement Learning & Simulation
PPO locomotion training and evaluation in Isaac Sim / Isaac Lab, with attention to what transfers and what doesn't.
Robot Learning & Autonomy
Navigation and control stacks where learned components meet classical robotics — and the evaluation to tell them apart.
Systems Integration
Multi-machine topologies: GPU inference, relay/control nodes, camera streaming, and SDK-level velocity control as one system.
Embedded & Edge Systems
FPGA, PCB, and microcontroller work — the layer where compute budgets, timing, and physics stop being abstractions.
AI Product Prototyping
Turning capability into products with real users and real constraints, informed by formal entrepreneurship training.
The next step is depth
01
The next systems need stronger foundations — optimization, learning theory, perception, control — than an undergraduate curriculum provides. A terminal master's is the direct path to that depth.
02
Deploying a vision-language model on a real humanoid surfaced questions about robustness, evaluation, and sim-to-real transfer that deserve proper study, not just engineering workarounds.
03
The long-term goal is building physical-AI products. Graduate study is leverage: research taste, harder problems, and an environment operating at a higher level.
Builder arc
2022 – 2027
Computer engineering foundation
Fort Lewis College · minors in mathematics and business
Core curriculum across circuits, embedded systems, digital design, and software — with math and business minors picked up deliberately alongside it.
2023 – 2024
Entrepreneurship track
President & co-founder, Entrepreneurial Ventures Association
Co-founded and led FLC's entrepreneurship organization to New Registered Student Organization of the Year; ran a campus pitch competition and brought the NASA Venture Program to campus. Goldman Sachs Emerging Leaders alum.
2024 – 2025
Applied-AI data platform
KDUR community radio · Power Apps, embeddings, Neo4j
Built and deployed the radio station's library and scheduling platform (60+ daily users), then prototyped the AI layer: vector embeddings for artist-name resolution and natural-language-to-Cypher agents.
Summer 2025
Robot revival and perception research
Sawyer/Baxter restoration · ROS · YOLOv8 thermal inspection
Restored two dormant industrial arms with no vendor support, then built a YOLOv8 pipeline for photovoltaic hotspot inspection — synthetic Blender training data, live validation on hardware.
Oct 2025 – Apr 2026
NASA-challenge hexapod
18-DoF autonomous walker · team lead of four
Led the electrical system, autonomous firmware, and Kuramoto-CPG locomotion simulation for a hexapod that competed at NASA's Colorado Robotics Challenge at the Great Sand Dunes.
May 2026 – present
Humanoid sim-to-real research
Booster K1 · NaVILA-style VLA + RL locomotion · Dr. Yiyan Li
Leading sim-to-real research on a real humanoid: a two-tier architecture pairing ~1 Hz vision-language planning with a 50 Hz reinforcement-learned locomotion policy trained in Isaac Sim / Isaac Lab.
Next · Fall 2027
Graduate study
Embodied AI / robotics
Deepen foundations in learning, perception, and control; sharpen research taste; build toward physical-AI systems that hold up outside the lab.
Technical reports
Systems are only as credible as their writeups. These are the reports this site is structured to hold.
Deployment report — vision-language navigation on a real humanoid
The intended home for a full writeup of the Booster K1 deployment: system topology, latency budget, failure modes encountered on hardware, and what the demo videos actually show.
Simulation & RL evaluation — what transfers and what doesn't
Planned analysis of PPO locomotion training in Isaac Sim / Isaac Lab: training setup, evaluation protocol, and an honest account of the sim-to-real gap.
Perception & navigation analysis
Reserved for a deep dive on a perception or navigation subsystem — sensing setup, metrics, edge cases, and where the pipeline breaks first.
Systems integration postmortem — the three-machine relay
Planned postmortem on connecting GPU inference, a relay/control node, and a robot into one control loop: the seams that failed, the debugging path, and the design tradeoffs that survived.
Working on physical AI — or reviewing an application?
Direct is best: email gets answered.