Boutique RL environments tailored for specific agentic behaviors. We provide the custom simulators and expert-tuned reward models needed for robust, targeted autonomy.
Expert-crafted environments designed for targeted policy convergence and real-world reliability.
Custom environments built on top of robust physics engines for specialized manipulation tasks. Includes highly-tuned sensor noise models for Sim-to-Real transfer.
Controlled arenas for training specific agent coordination behaviors. Optimized for analyzing negotiation and task-delegation among small groups of autonomous agents.
Focused data-loops that meticulously tune simulator parameters based on targeted physical hardware telemetry to minimize the reality gap for specific tasks.
We help labs move past standard evaluations into dynamic, task-specific autonomy through rigorous, physics-informed training setups.
Custom-designed, highly constrained reward functions (RLHF-for-RL) that prevent reward hacking and ensure safe, intended behavior.
Careful, systematic variation of specific friction, mass, and sensor parameters to train robust policies tailored for targeted real-world deployments.
Common questions about our bespoke simulation stack and RL training methodologies.