Language models got abundant data, cheap rollouts, and automatic grading. The physical world has none of these yet. Artha Labs builds the missing layer: simulation-ready scenes, graders, and training-ready data.
We focus on physical-world systems — robotics, embedded & edge intelligence, and autonomy — where the hard problems are data, evaluation, and the gap between simulation and reality. These are exactly the places where AI is the unlock, not the product.
We seed a handful of deep hypotheses each year, kill the ones that don't work fast, and give real time and capital to the ones that do.
Pre-training gives the base model; SFT and RL make it task-specific. RL from experience is where recent gains come from: π*0.6 (RECAP) more than doubles throughput on its hardest tasks. That loop needs simulators and reward functions robots don't have yet.
A platform (scenes, robots, datasets, rollouts, evaluation, training), a robotics lab for sim-to-real validation and dynamics calibration, physical data infrastructure, and human judges in the loop.
Builds shared commons and takes a stake when a prototype spins out as a venture. See Studio →
Task requirements + scene description + eval criteria go in. An agentic loop builds simulation-ready scenes and graders, and turns existing video into training-ready data, sim-to-real validated. Neutral to whose model you're training.
An interactive product prototype is live — explore Scenes, Robots, Datasets, Rollouts, Evaluation and Training.
Launch the prototype →Results proven on a real customer problem, then published.
Evaluation benchmarks and graders; contributions to open robotics stacks. Open-core platform with a free SaaS tier.
Research fellowships with placement at Artha Labs, grants to selected faculty, joint publications.
Agentic prototypes for India: wireframes and sector deep-dives. Studio takes a stake when one spins out. Browse all →