Been following the embodied AI space closely. A few specific pain points:

1. Few production-ready, general-purpose hardware options. Many robotic arms and dexterous hands on the market are still lab prototypes or small-batch custom builds.
2. Poor interoperability, fragmented ecosystem. Different manufacturers use wildly different interfaces, control precision, and sensor stacks.
3. Mismatched iteration speed between software and hardware. Algorithm-side progress (especially VLA models and large-scale imitation learning) is moving fast, but hardware iteration cycles are long and expensive.

Which raises the question: is embodied intelligence currently bottlenecked by algorithms/data, or by hardware itself?

My take is that the two are mutually constraining. Without enough hardware that’s cheap, plentiful, and standardized, it’s hard to collect real-world interaction data at scale; and without enough data, hardware makers lack the incentive to iterate toward AI-training-optimized designs.

Curious what others think.

In our paper we followed an experiment from developmental psychology and recreated it with a humanoid robot (iCub) that needs to actively explore its environment to fulfill its curiosity.

Photo credit Petr Neugebauer, FEL ČVUT.

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Does anyone have working promo / discount codes for Humanoids Summit Silicon Valley 2026 (Dec 1‑2, San Mateo Event Center)Humanoids ...?

Also checking: are there others on Reddit attending this humanoid & physical‑AI focused summit? Would love to connect ahead of time and meet IRL during the conference.

Drop a comment if you have a code or are going. Cheers.

Hace un par de horas me llegó este robot (Alpha mini 2) de ubetech pero aunque trae una app es únicamente con soporte para Asia y lo quiero programar si alguien lo ha usado y me puede dar recomendaciones gracias

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AGIBOT’s mass-produced humanoid robots dominated events from dexterous manipulation and obstacle racing to hotel services and emergency response.

At the World Humanoid Robot Games, a humanoid robot delivered surprisingly fast performance on the obstacle‑course track.

It dashed across gravel, steps and slopes while maintaining stable dynamic balance. Watching its speed makes you wonder: how far can humanoid locomotion really go? High‑speed movement pushes servos, gait planning and anti‑disturbance algorithms to their limits, yet falling risks remain a real challenge. Competitions like this offer eye‑opening real‑world benchmarks for humanoid robot research.

#HumanoidRobot #RoboticsCompetition