Using Fitts’ Law to Benchmark Assisted Human-Robot Performance
This research paper proposes a novel benchmarking method for shared control systems based on Fitts' Law to formally parameterize the difficulty of target-reaching teleoperation tasks. The authors conducted an empirical study with 24 participants to systematically model how task difficulty and robot autonomy influence task performance, perceived cognitive load, and user trust. The findings demonstrate that robot assistance generally improves task performance and reduces perceived workload, while introducing an adapted Fitts' law model to capture interactions between control interfaces and robot algorithms.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers and robotics engineers looking to benchmark and evaluate human-robot shared control interfaces and teleoperation performance.
What the document contains
The paper covers an empirical investigation into human-robot shared control using an adapted Fitts' Law framework, analyzing movement times, user-perceived cognitive workload via NASA-TLX, and trust via MDMT across varying target distances, sizes, and robot autonomy levels.
The paper does not provide regulatory compliance guidelines, legal definitions for autonomous systems, or real-time physiological metrics due to discarded eye-tracker data.
- Task performance decreases with greater task difficulty and increases with higher robot autonomy.
- Subjective cognitive load increases with greater task difficulty and decreases with higher robot autonomy.
- An adapted Fitts' Law equation can model the interaction between task difficulty, robot autonomy, and movement time in shared control systems.
- Users' trust in the robot generally increases under higher autonomy but shows a decreasing trend for high-autonomy tasks with greater difficulty.
- Medium levels of robot autonomy can help stabilize perceived cognitive load across varying task difficulties.
· I. Introduction · II. Related Works · III. Method · IV. Study Design · V. Results · VI. Discussion · VII. Conclusion
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
0/5Clear responsibility for AI outcomes
screening score · 2026-10-23
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-23
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-23
Transparency
0/5Openness about how AI systems work and are used
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-23
Impact Assessment
2/5Required assessment of an AI system's risks/impacts before or during use
We also found that the users’ perceived cognitive load and trust were influenced by these factors
Abstract
screening score · 2026-10-23