Researchers developed ProxiDex, a dynamics-guided proximity policy framework for dexterous manipulation, using a hardware-agnostic contact representation and action-conditioned proximity dynamics. This allows for improved success rates and robustness in manipulation tasks under unreliable visual feedback.