# TARC: Time-Adaptive Robotic Control

arXiv: https://arxiv.org/abs/2510.23176 · HTML: https://mlsanity.com/paper/2510.23176/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 51 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 30 of 32)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [DeepMind Control Suite](https://mlsanity.com/paper/1801.00690/) (2018)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [Steering Your Diffusion Policy with Latent Space Reinforcement Learning](https://mlsanity.com/paper/2506.15799/) (2025)
- [Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion](https://mlsanity.com/paper/2202.05481/) (2022)
- [MuJoCo Playground](https://mlsanity.com/paper/2502.08844/) (2025)
- [Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning](https://mlsanity.com/paper/1702.06054/) (2017)
- [AMZ Driverless: The Full Autonomous Racing System](https://mlsanity.com/paper/1905.05150/) (2019)
- [Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning](https://mlsanity.com/paper/2002.06836/) (2020)
- [Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods](https://mlsanity.com/paper/2111.03941/) (2021)
- [Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards](https://mlsanity.com/paper/2012.13658/) (2020)
- [Dynamic Decision Frequency with Continuous Options](https://mlsanity.com/paper/2212.04407/) (2022)
- [Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion](https://mlsanity.com/paper/2209.14887/) (2022)
- [Hold My Beer: Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control](https://mlsanity.com/paper/2505.24198/) (2025)
- [When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL](https://mlsanity.com/paper/2406.01163/) (2024)
- [Bridging the Sim-to-Real Gap with Bayesian Inference](https://mlsanity.com/paper/2403.16644/) (2024)
- [Gradient-Based Trajectory Optimization With Learned Dynamics](https://mlsanity.com/paper/2204.04558/) (2022)
- [Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning](https://mlsanity.com/paper/2311.07558/) (2023)
- [A Socially Adaptable Framework for Human-Robot Interaction](https://mlsanity.com/paper/2003.11410/) (2020)
- [Reinforcement Learning with Elastic Time Steps](https://mlsanity.com/paper/2402.14961/) (2024)
- [MOSEAC: Streamlined Variable Time Step Reinforcement Learning](https://mlsanity.com/paper/2406.01521/) (2024)

## Built on by (most cited first, 1 of 1)

- [Chronos Policy: Learning Robot Policies on Physical Time](https://mlsanity.com/paper/2610.13929/) (2026)

## Abstract (arXiv)

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 and unnecessary actuation. 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.