Minimalist Compliance Control

2026-02-28

AI Summary

This work proposes a minimalist compliance control approach that eliminates the need for dedicated force/torque sensors, which are typically costly and deployment-constrained. Instead, it leverages only the current/voltage signals from servo or quasi-direct-drive actuators, combined with Jacobian-based estimation of external forces, to implement a task-space admittance controller. The method requires no additional sensors, learning algorithms, or low-level current-loop modifications, offering plug-and-play deployment, inherent safety, and broad compatibility with diverse robotic platforms and high-level planners—including vision-language models, imitation learning, and model-based planning. Experimental validation on manipulators, dexterous hands, and humanoid robots demonstrates robust, rapid, and safe compliant interaction in contact-intensive tasks.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Interaction Techniques and Devices
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Compliance control is essential for safe physical interaction, yet its adoption is limited by hardware requirements such as force torque sensors. While recent reinforcement learning approaches aim to bypass these constraints, they often suffer from sim-to-real gaps, lack safety guarantees, and add system complexity. We propose Minimalist Compliance Control, which enables compliant behavior using only motor current or voltage signals readily available in modern servos and quasi-direct-drive motors, without force sensors, current control, or learning. External wrenches are estimated from actuator signals and Jacobians and incorporated into a task-space admittance controller, preserving sufficient force measurement accuracy for stable and responsive compliance control. Our method is embodiment-agnostic and plug-and-play with diverse high-level planners. We validate our approach on a robot arm, a dexterous hand, and two humanoid robots across multiple contact-rich tasks, using vision-language models, imitation learning, and model-based planning. The results demonstrate robust, safe, and compliant interaction across embodiments and planning paradigms.

Problem

• compliance control
• force sensing
• sim-to-real gap
• safety guarantees
• hardware constraints

Innovation

• Minimalist Compliance Control
• sensorless force estimation
• admittance control
• embodiment-agnostic
• plug-and-play compliance