Reference desk
Guides
Durable explanations designed to make a fast-moving technical field understandable without sanding away uncertainty.
Robot Foundation Models
Robot Benchmark Success Rates: A Practical Evidence Audit
A practical guide to reading robot benchmark results, using HumanCLAW, TurboVLA, SymmGrid, and ACT-2 to separate task scope, execution, trial counts, compute, and real-world evidence.
Read guide →Robot Foundation Models
Are Physical AI Benchmarks Measuring the Same Thing? A Redundancy and Ranking Audit
A practical audit of benchmark overlap, rank sensitivity, general capability, compact suites, and what current physical AI leaderboards do not establish.
Read guide →Robot Foundation Models
World Action Models for Robot Control: What Future Prediction Must Prove
An evidence guide to robot world models, including action compliance, compact latent intentions, control latency, physical trials, and simulation limits.
Read guide →Robot Foundation Models
VLA Control Interfaces: Prompt Authority, Spatial Grounding, Intent, and Memory
A practical evidence guide to four interfaces that shape vision-language-action control: prompt authority, typed spatial grounding, behavior intent, and event-selected memory.
Read guide →Robot Foundation Models
Can Robot Policies Learn from Failure? What Q-Planning Actually Proves
An evidence audit of Q-Planning, which uses a learned value model to improve action selection while keeping a large behavior-cloning robot policy frozen.
Read guide →Robot Foundation Models
Latent Actions for Robot Learning: What 41 Design Choices Actually Show
A practical evidence guide to latent action models, proxy metrics, action integration, data scaling, and the limits of a 59-million-frame robot learning study.
Read guide →Robot Foundation Models
The Embodiment Gap in Robot Foundation Models: What Still Changes on the Target Robot
A practical guide to cross-embodiment robot claims, covering reusable semantics, data, correspondence, target-robot adaptation, calibration, control, contact, safety, recovery, and evaluation.
Read guide →Robot Safety
Adaptive Robot Safety Cases: Specifications, Monitoring, and Evidence
A practical guide to building an evidence-backed safety case for an adaptive robot, using ManiGuard to separate task success, safe success, specifications, runtime monitoring, distribution shift, and field validation.
Read guide →Robot Foundation Models
Long-Horizon Robot Control: Planning, Memory, and Whole-Body Execution
An evidence guide to long-horizon robot control, using Tau-0-VLA and HAF to separate subtask planning, execution memory, low-level action, whole-body coordination, and online adaptation.
Read guide →Robot Foundation Models
Dynamic Robot Benchmarks: How to Measure Latency, Recovery, and Process Quality
A practical evidence guide to dynamic robot evaluation, using ReflexBench and PRM-as-a-Judge 1.5 to measure reaction timing, progress, regression, recovery, and execution quality.
Read guide →Robot Learning
LeRobot 0.6 Practical Guide: Models, Evaluation, and Human Correction
A source-backed guide to LeRobot v0.6.0 and v0.6.1, including models, evaluation integrity, real-robot rollouts, human correction, operator controls, and cadence reporting.
Read guide →Robot Learning
How Robot Training Data Is Made: Teleoperation, Egocentric Video, EEG, and EMG
A source-backed guide to robot training data, including real-robot, UMI, human video, simulation, and general data, plus labels, interventions, costs, and evidence quality.
Read guide →Robot Learning
When Should a Robot Hand Control to a Human? An Intervention Evidence Guide
A practical guide to robot intervention evidence, using AutoIntervene to separate handoff detection, operator time, recovery, corrective data, and post-adaptation task success.
Read guide →Embodied AI
Physical AI Funding Tracker: What Has the Capital Actually Produced?
A source-backed comparison of Unitree, Atoms, Humanoid, Walden, Apptronik, Agility Robotics, Gritt, and Sereact by capital, manufacturing, customers, and operating evidence.
Read guide →Robot Foundation Models
Xiaomi-Robotics-1: What 100,000 Hours of Training Data Actually Prove
A source-backed audit of Xiaomi-Robotics-1, including its 100,000-hour UMI corpus, released code and 5B checkpoint, benchmark protocols, and missing evidence.
Read guide →Humanoid Robots
Humanoid Robots as a Service: What a 90-Day Pilot Must Prove
A buyer's guide to humanoid Robots-as-a-Service, using Agility Robotics' deployment process to separate setup, pilot validation, production measurement, support, safety, and ROI evidence.
Read guide →Robot Learning
Ego2Robot: What 18,561 Hours of Synthetic Robot Data Actually Mean
An evidence audit of Ego2Robot, covering its 1,940 source hours, 15 rendered morphologies, quality filters, fixed-frame comparisons, real-robot tests, artifacts, and limits.
Read guide →Robot Foundation Models
Gemini Robotics 2: What the Whole-Body, Reasoning, and On-Device Models Actually Prove
An evidence audit of Google DeepMind's three Gemini Robotics 2 models, covering whole-body control, embodied reasoning, on-device adaptation, access, safety, and missing deployment evidence.
Read guide →Humanoid Robots
Humanoid Robots in 2026: What Is Actually Available, Piloting, or Deployed?
An evidence-based comparison of leading humanoid robots, including Digit, Figure, AGIBOT G2, Atlas, Apollo, NEO, Phoenix, and Unitree, with demonstrations separated from pilots, sales, and production deployments.
Read guide →Robot Policy
FCC Foreign-Robot Restrictions: What the Covered List Changes for U.S. Buyers
A source-backed guide to the FCC's July 2026 restrictions on foreign-produced mobile robots, including scope, existing models, Conditional Approvals, software updates, and buyer due diligence.
Read guide →Robot Safety
Robot Safety Standards for Working Near People
A source-backed guide to ISO 10218, ISO 13482, ISO 3691-4, collaborative robot applications, risk assessment, and choosing the right safety framework for industrial robots, AMRs, service robots, and humanoids.
Read guide →Foundations
What Is Physical AI?
A practical explanation of AI that perceives, reasons about, and acts in the physical world—from embodied intelligence to robot control.
Read guide →Robot Foundation Models
FLUX-mimic: What the Video-Action Model and Audi Evidence Actually Prove
A source-backed audit of FLUX-mimic, including its FLUX 3 video backbone, action decoder, 20-trial benchmark, edge latency, Audi work, model access, and missing operating evidence.
Read guide →Embodied AI
Physical AI in Production: Six Deployments Compared by Evidence
A source-backed comparison of Gritt, Sereact, Monumental, Digit, Figure 02, and AGIBOT G2 across task, customer confirmation, fleet, runtime, throughput, intervention, safety, and commercial access.
Read guide →Robot Foundation Models
Sunday Robotics ACT-2: What 778 of 785 Laundry Folds Actually Prove
A source-backed audit of ACT-2’s reported 99.1% laundry-folding result, including scope, adaptation cost, grading, speed, generalization claims, and the evidence still needed from home beta testing.
Read guide →Industrial Robots
Walden Robotics Launch: What the Toyota Factory Evidence Actually Proves
A source-backed analysis of Walden Robotics’ $300 million launch, Toyota factory operation, wheeled robot, AI stack, commercial access, and the deployment evidence still missing.
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