cs.RONov 1, 2026

Human Sensorimotor Model Adaptation to Account for Robot Accuracy and Speed Augmentation in Human-Robot Co-Manipulation

Authors: Frits van Hall, Kevin Haninger, Luka Peternel

Organizations: Cognitive Robotics, Delft University of Technology, Delft, The Netherlands · Department of Automation, Fraunhofer IPK, Berlin, Germany

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Abstract

In human-robot co-manipulation, the robot's trajectory should accommodate the human's, where human sensorimotor behavior is affected by the task's spatial and temporal constraints, as well as personal factors. Several robot trajectory planning methods have been proposed to embed human sensorimotor models in the robot trajectory planning, but these methods do not consider temporal constraints that adapt to how the human behaves when interacting with the robot. To better align human and robot collaborative motion, this work adapts a stochastic optimal control problem with a final state constraint from the task constraints and a temporal constraint from Fitts' law. A recursive least squares method is used to estimate the Fitts' law parameters, adapting the robot's motion duration to that of the human, thus allowing the robot to adapt to how the human behaves when interacting with the robot. It is hypothesized that a speed increase can result from the robot augmenting the accuracy. The feasibility of the approach is validated on a positioning task where the robot controls degrees of freedom which have repeatable motion, and the human adapts motion in the complementary degrees of freedom. The approach is shown to be computationally suited to online use, it converges as the human and robot mutually adapt, and can increase the movement speed, although thorough human studies are needed in future work.

Figures & tables

Figure 1: The proposed sensorimotor planner generates a human-like trajectory (blue) to a authority handover plane (red), where the robot controls the vertical z-axis motion with a high impedance, and the human controls the horizontal x/y-plane motion. By including the known handover plane as a final state constraint, the robot eliminates the primary source of task risk, which is collisions with the working plane (e.g., assembly plane) (yellow). The exact position within the plane (dark green) is determined by the human to adapt to various goal positions (diverging arrows, light green). The robot’s motion should be synchronized so the human doesn’t need to wait for the robot, even when a new goal location is found. The proposed planner includes a Fitts’-law-based temporal constraint, adapting it online using observed movement durations and distances.
Figure 2: Overview of the proposed sensorimotor planning framework. The planner models aspects of human motor control and task constraints. Dashed lines indicate which aspect the planner components infer or model. The temporal constraint models how humans determine movement duration from task difficulty and adapts to speed increases from robotic augmentation; the scaled stochastic model embodies human motor control principles to generate smooth robot trajectories; the final state constraint exploits the known authority handover plane position to suppress endpoint variability for accuracy augmentation. The solid line represents the physical force interaction between the impedance controller and the human body during co-manipulation.
Figure 3: End-effector velocity profiles for both sets of simulated tasks: Set A (left) and Set B (right).
Set A: fixed width W=0.100 m, varying distance
Distance (m)Width (m)Duration (s)Solve time (s)
0.1500.1000.85001.6802
0.3000.1000.97002.0249
0.4500.1001.04002.6004
0.6000.1001.09003.3500
––0.9875±0.09010.9875\pm 0.09012.4139±0.63262.4139\pm 0.6326
Table 1: Task statistics for movements with varying distance and width.
Figure 4: Measured movement data and RLS parameter adaptation for baseline (Condition A, red) and the proposed method (Condition B,green). Each dot represents a single trial, and each line shows the Fitts’ law prediction after an RLS update. The initial Fitts’ law relationship is indicated by the blue dotted line (a = 0.3, b = 0.35).
Figure 5: Final Fitts’ law relationships after 15 RLS updates for both conditions, showing predicted movement duration at constant task difficulty (ID = 2.47).
Figure 6: Endpoint trajectories (left) and velocity profiles (right) from Model A [ 10 ] and Model B [ 16 ] for targets (black circles) at five different distances and a constant target width. The 2D ellipses represent the predicted endpoint covariance from signal-dependent noise, not distributions from multiple trajectory samples. Vertical lines in the velocity profiles indicate durations predicted by Fitts’ law with parameters a=0.65a=0.65 , b=0.12b=0.12 .
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