Robotics take: IK guarantees no precision edge, hardware—not the solver—is the bottleneck
ChongZzZhang · x · 2026-10-20
ChongZzZhang on robot learning methods: assuming perfect modeling, RL-style methods that take gradients through differentiable dynamics have negligible error versus MPC — the real bottleneck is hardware, not the solver. Some people wrongly believe inverse kinematics guarantees better precision than RL.
He adds that directly using dynamics for gradients "cheats" toward differentiable RL, but is less of a cheat than MPC and enables real domain randomization.
A technically substantive point: the precision ceiling is hardware, not the method.
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