Robotics & Embodied AI
Vision-language-action models, action tokenisation, diffusion policies and sim-to-real.
10concepts
122flashcards
80minutes of reading
- 01 Action Tokenisation and Representation How continuous robot actions become discrete tokens an autoregressive transformer can emit, from per-dimension binning to frequency-space compression.
- 02 Evaluating Robot Policies Why a reported 70 percent success rate on a robot task usually carries an error bar of plus or minus 15 points, and what protocol changes make two policies actually comparable.
- 03 Imitation Learning and Diffusion Policies Why cloning a demonstrator's actions drifts into unseen states, and how generative action models such as diffusion policies and action chunking control the drift.
- 04 Massively Parallel Simulation and Sim-to-Real Locomotion How putting the physics engine on the GPU turned weeks of legged-robot RL into minutes, and why domain randomisation is the only reason those policies survive contact with a real floor.
- 05 Sim-to-Real Transfer Why robot policies are trained in simulation, why they break on real hardware, and how domain randomisation closes the reality gap by making the real world look like one more random draw.