Sensorless contact wrench estimation for industrial robots

Tue, 11 Aug 2026 00:00:00 +0000

Robots used for polishing, assembly, and manipulation need reliable contact-force feedback. A six-axis wrist force/torque sensor provides direct measurements, but adds cost, wiring, payload, and integration constraints. This project estimates the same six-dimensional contact wrench from signals already available on the robot: joint motion and motor current.

Dual-stage estimation

The estimator combines a physics-based robot model with two learned residual corrections:

Free-space dynamics compensation. An LSTM learns joint-torque errors caused by friction, backlash, hysteresis, parameter mismatch, and torque-conversion bias using contact-free trajectories.
Contact wrench compensation. A temporal encoder and residual network correct the remaining task-space error during contact and predict an input-dependent uncertainty for each wrench axis.

Separating these responsibilities keeps the estimate anchored to robot mechanics while allowing each learning stage to focus on a distinct error source.

Experimental validation

The method was evaluated on a six-axis industrial manipulator. A cable-and-pulley rig applied loads of 0.5, 1.0, and 1.5 kg from multiple directions. A wrist load cell sampled at 1 kHz supplied training labels and evaluation ground truth, but was not used as an input during sensorless inference.

Stage I reduced the Joint 1 residual-torque RMSE from 4.2649 to 0.8591 Nm and the Joint 2 RMSE from 3.1453 to 1.0069 Nm. Across the final six-axis wrench evaluation, the dual-stage method achieved the lowest reported RMSE, maximum error, and relative error on every force and moment axis.

Metric	Fx	Fy	Fz	Mx	My	Mz
RMSE	1.5959 N	0.7067 N	1.9678 N	0.1367 Nm	0.1141 Nm	0.0538 Nm
Maximum error	5.9546 N	4.4262 N	5.3414 N	0.7739 Nm	0.4898 Nm	0.4104 Nm
Relative error	13.8%	5.8%	13.4%	17.6%	22.2%	8.3%

Why it matters

The main contribution is not simply a deeper network, but a physically meaningful decomposition of the estimation problem. The intermediate output makes each correction inspectable, while the probabilistic second stage provides a condition-dependent confidence signal. This creates a practical path toward lower-cost force-aware robots and future closed-loop force control without a permanently installed wrist sensor.

Machine Learning | MAAL

Sensorless contact wrench estimation for industrial robots

Dual-stage estimation

Experimental validation

Why it matters