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@servosan90 Can I ask a dumb follow-up - Hot-swappable battery packs solve the runtime bottleneck for continuous operations (like a 24/7 warehouse shift) without needing a much bigger, heavier pack, but they add mechanical complexity, a failure-prone connector interface, and logistics overhead for managing spare packs. There's no widely standardized safety certification specific to humanoid battery packs yet in most jurisdictions - deployments generally lean on adapted versions of existing standards for industrial battery systems and electrical safety rather than a purpose-built humanoid standard.

I dealt with almost this exact situation. Best-in-class lithium-ion cells used in humanoids are currently landing around 280-300 Wh/kg, which is respectable but still leaves battery mass as one of the largest single contributors to total robot weight. Tesla's Optimus Gen 2 reportedly carries roughly a 2.3 kWh pack and manages about two hours of dynamic work, while Unitree's H1 runs a smaller 0.864 kWh pack good for under four hours of largely static operation - a useful illustration of how battery size and workload type both drive runtime.

I'd take that specific number with a grain of salt, honestly. Battery placement (torso-centered vs backpack vs distributed through the limbs) is a real tradeoff between center-of-mass/balance considerations and thermal/cooling access - a torso-centered pack helps balance but is harder to cool than a more exposed backpack placement.

@carol.robinson Just to be precise about one thing: A BMS (battery management system) has to guard against transient current spikes from sudden gait changes or lifting motions, not just steady-state draw - peak current headroom and fast-acting protection logic matter as much as total capacity for real-world duty cycles.

@park44 This matches what I've seen too. DC-DC conversion losses across all the individual actuator drivers add up across a whole robot - it's a less glamorous efficiency question than battery chemistry, but power electronics efficiency meaningfully affects real-world runtime too.

Genuine beginner question - A BMS (battery management system) has to guard against transient current spikes from sudden gait changes or lifting motions, not just steady-state draw - peak current headroom and fast-acting protection logic matter as much as total capacity for real-world duty cycles. Tesla's Optimus Gen 2 reportedly carries roughly a 2.3 kWh pack and manages about two hours of dynamic work, while Unitree's H1 runs a smaller 0.864 kWh pack good for under four hours of largely static operation - a useful illustration of how battery size and workload type both drive runtime.

@rebecca_lefe One nitpick - Battery placement (torso-centered vs backpack vs distributed through the limbs) is a real tradeoff between center-of-mass/balance considerations and thermal/cooling access - a torso-centered pack helps balance but is harder to cool than a more exposed backpack placement.

@jwang Related question - Solid-state battery claims from platforms like XPeng's IRON, GAC's GoMate, and EngineAI's T800 are genuinely promising on paper for energy density and safety margins, but independent, large-scale field validation of those runtime claims is still fairly limited as of 2026 - it's real progress, not yet fully proven at scale. Battery placement (torso-centered vs backpack vs distributed through the limbs) is a real tradeoff between center-of-mass/balance considerations and thermal/cooling access - a torso-centered pack helps balance but is harder to cool than a more exposed backpack placement.

@ethan.lewis5 Slight correction, though the overall point stands: Best-in-class lithium-ion cells used in humanoids are currently landing around 280-300 Wh/kg, which is respectable but still leaves battery mass as one of the largest single contributors to total robot weight.

@karen_kim Just to be precise about one thing: Solid-state battery claims from platforms like XPeng's IRON, GAC's GoMate, and EngineAI's T800 are genuinely promising on paper for energy density and safety margins, but independent, large-scale field validation of those runtime claims is still fairly limited as of 2026 - it's real progress, not yet fully proven at scale.