Claude Academy ↗
Anthropic’s courses, tutorials and AI fluency resources.
Physical AI
New research: robot recovery, data interfaces and executable motion →
From coordinate frames to robot foundation models. A connected study and interview-preparation path with diagrams, equations, worked examples and evidence-based project checkpoints.
These are original learning exercises, not company interview reports. Reading supports preparation; hiring depends on practical skills and role fit. Articles and diagrams work without uploads. Proposed robot-training experiments require your own simulation or hardware environment.
Choose a role, then build evidence
Robot Learning / VLA · 1 → 4 → 5 → 6 → 7 → 8. Deliver a dataset audit, baseline and ablation report.
Perception / Controls · 1 → 2 → 3 → 8. Deliver calibration, estimation and disturbance-response tests.
Robotics Infrastructure · 2 → 3 → 6 → 8. Deliver timestamp contracts, action adapters and latency/failure dashboards.
- 1
01 · Frames, transformations and robot kinematics
Translate a camera observation into a robot command without losing frame, unit or rotation conventions.
Prerequisites: Vectors, matrix multiplication, radians.
Read post & exercises → - 2
02 · Perception, state estimation and time
Understand why a sharp image can still produce a stale, uncertain control state.
Prerequisites: Frames, probability and covariance.
Read post & exercises → - 3
03 · Feedback control and receding-horizon planning
Connect learned actions to stable feedback and constrained execution.
Prerequisites: Dynamics, derivatives and linear algebra.
Read post & exercises → - 4
04 · Demonstrations, behavior cloning and ACT
Design a demonstration dataset and explain why action chunks help without hiding distribution shift.
Prerequisites: Supervised learning, sequence models and control frequency.
Read post & exercises → - 5
05 · Diffusion Policy and multimodal actions
Explain why averaging demonstrations can fail and how conditional denoising represents alternatives.
Prerequisites: Behavior cloning, Gaussian noise and sequence prediction.
Read post & exercises → - 6
06 · OpenVLA: vision, language and action interfaces
Trace a language-conditioned robot policy through action encoding, normalization and embodiment.
Prerequisites: Transformers, imitation learning and robot frames.
Read post & exercises → - 7
07 · π₀ and continuous action flow matching
Distinguish token prediction, diffusion noise prediction and a continuous velocity field.
Prerequisites: VLA interfaces, differential equations and conditional generation.
Read post & exercises → - 8
08 · Robot RL, sim-to-real and deployment evidence
Design rewards and evaluations that reveal failure before making claims about real robots.
Prerequisites: MDPs, PPO, control constraints and statistical evaluation.
Read post & exercises →
Interview readiness: demonstrate, do not memorize
For every topic: derive the key equation without notes, reproduce the numeric example, implement one bounded test, and explain a failure. Score each dimension 0 (not yet), 1 (with help), or 2 (independently). A high self-score is a practice signal, not a hiring prediction.
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