Simon Kalouche
@simonkalouche • 10,485 subscribers
Founder, https://t.co/kcPDZaNUuP. Building general superhumanoids to power autonomous supply chains. Prev: Stanford PhD student advised by @drfeifei
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Boston Dynamics is so far ahead. Because demo videos are easy, only roboticists actually know this.
Simon Kalouche1,309,820 Aufrufe • vor 1 Jahr
Very creative concept to autonomously fabricate parts and assemble them entirely on a 3D Printer
Simon Kalouche840,670 Aufrufe • vor 1 Jahr
Simple, clever design by Unitree. Electrical / Wiring: • Pogo pins for modular board-to-board connections • Rigid-flex PCBs for dense packaging • Connectors retained with silicone adhesive for vibration resistance • Locking & polarized plugs • Permanent joints epoxied for reliability • Cloth tape for strain relief and cable bundling • Harnesses routed cleanly and taped down for vibration control • Pokayoke connector layouts • Shared signal + power connectors to simplify harnessing Mechanical / Structure: • Injection-molded or casted housings • Clamshell assemblies reduce fastener count and ease service • Integrated standoffs, guides, and cable paths in castings • Torx fasteners • Carbon-fiber linkage for low inertia driven by partial spur gear with hard stops • Wear components designed for quick replacement
Simon Kalouche75,776 Aufrufe • vor 8 Monaten
Mobile manipulator playing badminton using model-free RL policy
Simon Kalouche91,302 Aufrufe • vor 1 Jahr
Data has always been the bottleneck for physical AI in self driving and robotics. Tesla is taking two very different approaches for FSD and Optimus. Tesla’s Optimus Training Playbook: 1. Build 30k Optimus Gen 3 robots 2. Operate them in a mock environment where they can perform self-play “Optimus Academy” 3. Train in sim using the real robot data to close sim2real gap. Tesla FSD Training Playbook: 1. Sell millions of cars outfitted with cheap cameras 2. Collect diverse real world driving data (especially intervention and failure recovery data) for free as a byproduct of customers driving the cars. 3. Use driving data to train Autopilot/FSD and deploy policies incrementally as a supervised FSD product 4. Repeat until policy reaches robust unsupervised full self driving for robotaxi launch. The Tesla FSD playbook is a beautiful self-funding, customer subsidized, diverse real world data flywheel. The Optimus playbook is the opposite and shares none of the beautiful attributes of the FSD training flywheel that made FSD successful. The key differences: 1. Instead of having customers pay you for vehicles, Tesla will need to fund 30,000 Optimus robots. Assuming the current landed cost per unit is $100k, that will be $3B to build plus another ~30% per year for maintenance labor and spare parts given it’s still an unhardened pre-production prototype is another $900M per year. For reference, Tesla’s GAAP net income in 2025 was $3.8B. 2. Instead of having customers drive their Teslas on roads all across the world giving Tesla an insanely rich and diverse dataset that Waymo and other AV companies could never collect, the Optimus Academy is doing the equivalent of building a fake town in a parking lot and driving their car in that parking lot. No matter how real you try to make the environments for self-play you can never replicate the diversity, complexity and failure modes of the real world. Data collected in staged environments produces demo-grade policies and will not be rich enough to generalize to the vast diversity of environments, tasks, objects, etc. out of distribution. 3. Instead of having customers collect real world failure recovery data (DAgger style) for free every time FSD disengages, the Optimus Academy will need paid teleoperators or onsite operators to collect the recovery data. Assuming 1 person can manage 2 robots to start that would cost $3.5B in labor per year (30,000 robots, $40/hr fully loaded, 16 hrs/day, 365 days per year, 2:1 robot:operator). Tesla can come up with the money to do this but money doesn’t solve the “mock data” problem. Given the higher degrees of freedom in humanoids vs. cars, training a generalized humanoid will be harder and require more data than a self-driving vehicle. The best way to train your robot is by deploying them in the diverse real world, subsidized by real customer operations. Humanoids face a chicken and egg where it’s very hard to bootstrap your way to a first policy that’s good enough to deploy in real production environments. This is an extremely capital intensive playbook (which doesn’t even include cost of training). Time will tell if it works but a better playbook would be finding a way to copy the FSD playbook.
Simon Kalouche34,671 Aufrufe • vor 3 Monaten
I guess we’ll have the Terminator before we have our sock folding robot.
Simon Kalouche103,696 Aufrufe • vor 1 Jahr
Humanoids will be out of jobs before they can even do jobs.
Simon Kalouche64,700 Aufrufe • vor 1 Jahr
