In progress (Last updated: 04/29/25)
Building truly useful in-home service robots by hacking human habits.

Part 1: Habit Learning Cognitive Architecture. We all have countless habits, big and small, that keep our lives efficient. By using the latest machine learning technology, all of these habits can be picked up by your robot. The best helpers anticipate; not react.
Part 2: Ultramobile Wheelbase. Currently, most attention is focused on legged robots for inside the home. However, wheelbases provide greater efficiency, stability, and accuracy. Existing wheelbases require a large footprints and cannot negotiate common household and urban obstacles such as bumps and stairs.
I’m going to fix this and build the world’s best wheelbase platform.
Update #2: April 20.
Mechanical design progress. Designed for manufacturability and scalability (up to 10kg allowance for robot arms). Cognitive architecture plan write-up added to site.

Update #1: April 15.
Simplified stair-climber successfully climbs standard stairs given movement primitives and stair dimensions. Video attached below for reference.
Methodology
Previous Relevant Projects:
Autonomous wheelbase package delivery robot (Credits to Adam Omarali, bracket.bot)
Tested stair climbing mechanism using MuJoCo.
Readings
Interactive Continual Learning: . https://arxiv.org/pdf/2403.03462
LLM Integration for home service robots:
https://arxiv.org/pdf/2305.05658
Few Shot Class Incremental Learning:
https://arxiv.org/pdf/2404.02117





