# Fine Robotic Manipulation without Force/Torque Sensor

arXiv: https://arxiv.org/abs/2301.13413 · HTML: https://mlsanity.com/paper/2301.13413/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 44 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Builds on (most cited first, 5 of 5)

- [A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control](https://mlsanity.com/paper/1805.08576/) (2018)
- [Learning Sequences of Manipulation Primitives for Robotic Assembly](https://mlsanity.com/paper/2011.00778/) (2020)
- [A Recurrent Convolutional Neural Network Approach for Sensorless Force Estimation in Robotic Surgery](https://mlsanity.com/paper/1805.08545/) (2018)
- [Online Visual Robot Tracking and Identification using Deep LSTM Networks](https://mlsanity.com/paper/1810.04941/) (2018)
- [Convex Controller Synthesis for Robot Contact](https://mlsanity.com/paper/1909.04313/) (2019)

## Built on by (most cited first, 2 of 2)

- [TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models](https://mlsanity.com/paper/2509.07962/) (2025)
- [Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation](https://mlsanity.com/paper/2609.13779/) (2026)

## Abstract (arXiv)

Force Sensing and Force Control are essential to many industrial applications. Typically, a 6-axis Force/Torque (F/T) sensor is mounted between the robot's wrist and the end-effector in order to measure the forces and torques exerted by the environment onto the robot (the external wrench). Although a typical 6-axis F/T sensor can provide highly accurate measurements, it is expensive and vulnerable to drift and external impacts. Existing methods aiming at estimating the external wrench using only the robot's internal signals are limited in scope: for example, wrench estimation accuracy was mostly validated in free-space motions and simple contacts as opposed to tasks like assembly that require high-precision force control. Here we present a Neural Network based method and argue that by devoting particular attention to the training data structure, it is possible to accurately estimate the external wrench in a wide range of scenarios based solely on internal signals. As an illustration, we demonstrate a pin insertion experiment with 100-micron clearance and a hand-guiding experiment, both performed without external F/T sensors or joint torque sensors. Our result opens the possibility of equipping the existing 2.7 million industrial robots with Force Sensing and Force Control capabilities without any additional hardware.