Learning-Enhanced Observer for Sensorless Task-Null-Space Compliant Control
Authors: Nuo Chen, Ya-Jun Pan, Onur Toker
Organizations: Advanced Control and Mechatronics Lab, Department of Mechanical Engineering, Dalhousie University, Halifax, B3H 4R2, Canada · Department of Computer Engineering, Florida Polytechnic University, Lakeland, FL 33805, United States
This paper presents a novel framework integrating a robust wrench observer with a hybrid pose and force controller for physical Human-Robot Interaction (pHRI). In pHRI tasks, robots are expected to perform task-space motions robustly while remaining compliance with human disturbance, which proposes distinct control objectives. Most existing approaches focus mainly on either robustness or compliance and rely on force sensors for real-time tracking. To address these limitations, we propose a novel framework with the following key contributions. Firstly, a neural network (NN) is trained to improve the observer performance by compensating for system friction and unmodeled dynamics. Secondly, a task-null-space decomposition is applied to separate the joint velocity and reduce the effect of the human disturbance on the observer. Finally, a hybrid pose and force controller is introduced to achieve the end-effector pose and force tracking. Experimental results demonstrate that the proposed framework provides robust and accurate wrench estimation while maintaining reliable pose and force tracking performance. The proposed approach offers an effective solution and framework for pHRI without the need of force/torque sensor measurements which can be applied to many robotic manipulator platforms which do not have force/torque sensors.
Figures & tables
Fig. 1: Overview of the proposed framework. The observer estimates the end-effector force using a momentum observer and NN compensation. A task-null-space decomposition separates the joint velocity q˙ into the task-space velocity q˙t and null-space velocity q˙n . The task-space velocity q˙t is sent to the NN to reduce the effect of human disturbance on force estimation, while q˙n is used for null-space compliant control. The proposed hybrid pose and force controller provides reliable position, orientation, and force tracking while maintaining compliance in the null-space.
Fig. 2: Hardware Setup: (a) A 7-DOF Franka Emika robotic manipulator in the Advanced Control and Mechatronics Laboratory, Dalhousie University. (b) An end-effector installed with force sensor for data collection and validation.
Fig. 3: Conventional momentum observer sim-to-real gap. (a) Test in Gazebo. (b) Test in real world. (c) Observer output difference between the simulation and real world.
Fig. 4: Comparison of the end-effector force estimation performance under human applied disturbances. The conventional momentum observer, the learning-based observer using the full joint velocity, and the proposed observer were compared with sensor force. The enlarged regions within box lines highlight the estimation performance during human disturbance periods at around T=14s and 27s respectively.
Observer
Overall
Region 1
Region 2
Maximum
RMSE(N)
RMSE(N)
RMSE(N)
Error(N)
F^mz
7.513
9.334
3.103
12.253
F^Φ(q,q˙)z
0.960
2.016
0.878
3.054
F^Φ(q,q˙t)z
0.729
0.804
0.408
1.892
TABLE I: Comparisons of the end-effector force estimation performance
Fig. 5: Controller tracking performance with two reference trajectories (a) R1 and (b) R2 . The translational position tracking errors Px , Py , and Pz are along the x, y and z directions. The orientation tracking errors Oψ , Oθ , and Oϕ are yaw, pitch, and roll. The force tracking errors Fz are along the z direction.
Fig. 6: Reference trajectories R1 and R2 . (a) R1 is a circular trajectory and (b) R2 is a rounded trapezoidal trajectory.
Physical human-robot interaction requires yielding transiently to contact yet recovering the commanded reference under sustained load. Finite-stiffness impedance control retains a static deflection there, while predictive alternatives typically optimize a nonlinear robot or impedance model online. Operational-space cancellation instead exposes a translational error double integrator with a fixed transition matrix and a configuration-scheduled input map, making interaction a predictive quantity rather than a property re-derived per configuration. We build on it a compact offset-free interaction-error MPC for torque-controlled manipulators: a force-domain random-walk state estimates persistent interaction and model error, and a 30-variable convex QP maps the correction through the current task inertia while constraining the applied joint torque. Conditional results establish impedance equivalence of the unconstrained passive feedback, offset-free regulation at feasible frozen configurations, and quadratic stabilizability of the scheduled backbone. In a 1kHz MuJoCo simulation of a 7-DOF Franka FR3, the estimator cuts steady-state error under a repeated 15N step from 2.77mm to 0.042mm when added to the otherwise identical 100Hz MPC. A stiffness-and-damping-calibrated impedance baseline attains 2.59mm but briefly saturates and needs 3.3x the peak positive joint power. Adding ideal measured-force cancellation to that baseline gives 1.39mm, so constant-load rejection is not unique to MPC; the sensorless controller still reaches 0.042mm in the moving task, a 65x reduction without force sensing and without the baseline's saturation or power cost. Demonstrated in simulation under a shared actuator budget, the contribution is an efficient operational-space realization complementing rather than replacing broader interaction-control architectures.
Yongyan Cao, Jinshan Tang
Voryx Robotics LLC, San Jose, CA 95136, USA · Department of Health Administration and Policy, George Mason University, Fairfax, VA, USA
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.
Force-controlled loco-manipulation requires a whole-body policy to coordinate locomotion and arm motion while regulating end-effector interaction forces. This is challenging under floating-base dynamics and changing support contacts, particularly when end-effector force/torque sensing is unavailable for control. This paper presents a force-aware reinforcement learning approach with hybrid sensorless force estimation for wheeled-legged loco-manipulation. The proposed method provides a structured estimate of the end-effector force as an explicit policy observation, enabling force-guided contact behavior without using an end-effector force/torque sensor for control. The force estimate is obtained by combining generalized momentum observation, contact-constrained wrench projection, and temporal residual learning: the model-based components extract the physically structured part of the whole-body disturbance, while the residual network compensates the remaining motion-dependent bias. The estimated force is integrated into a mode-conditioned whole-body policy with an axis-wise force/position selector, allowing free-space motion, pure force regulation, and hybrid force/position control within one controller. Simulation results demonstrate improved sensorless force estimation and force-control performance. Hardware experiments further validate the proposed controller through quantitative valve-rotation and hybrid wiping evaluations, together with force-guided door opening and zero-force human-guided motion on a real wheeled-legged platform.
Xuanqi Zeng, Jiaming Wang, Tianlin Zhang +5
Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong, Shatin, Hong Kong