Publications

2026

1. 

   TARC: Time-Adaptive Robotic Control

   Arnav Sukhija, Lenart Treven, Jin Cheng, and 3 more authors

   Conference on Robot Learning (CoRL), 2026

   Abs arXiv Bib Website

   Most robotic systems rely on fixed-frequency discrete-time controllers, creating a trade-off between the efficiency of low-frequency control and the responsiveness of high-frequency feedback. As a result, systems typically default to high control rates for robustness, at the cost of wasted inference bandwidth, actuation jitter, and energy consumption. Addressing this, we introduce Time-Adaptive Robotic Control (TARC), a reinforcement learning framework in which the policy jointly predicts a control action and its duration of application. TARC learns temporally extended actions by optimizing task performance under soft or hard constraints on the number of control switches, enabling adaptive modulation of control rates. We evaluate TARC on two robotic hardware platforms: a high-speed RC car and the Unitree Go1 quadruped, and on a vision-language action model in simulation, where each query incurs a costly transformer forward pass. Across all settings, TARC matches the performance of high-frequency discrete-time controllers while operating at less than half their control frequency. Unlike fixed-rate controllers, TARC adapts its control frequency online, allocating high-frequency feedback only when required.

   @article{sukhija2025tarc,
     title = {TARC: Time-Adaptive Robotic Control},
     author = {Sukhija, Arnav and Treven, Lenart and Cheng, Jin and D{\"o}rfler, Florian and Coros, Stelian and Krause, Andreas},
     journal = {Conference on Robot Learning (CoRL)},
     year = {2026},
     video = {https://youtu.be/lTcANSTLAYU}
   }