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[2602.18850] When the Inference Meets the Explicitness or Why Multimodality Can Make Us Forget About the Perfect Predictor
Summary
This paper explores the effectiveness of multimodal communication systems in human-robot collaboration, analyzing how explicit communication can enhance performance in tasks requiring human intention prediction.
Why It Matters
Understanding the interaction between human intention and robotic systems is crucial for developing more effective collaborative robots. This research highlights the importance of combining predictive models with explicit communication methods to improve task performance and user satisfaction.
Key Takeaways
- Human performance in collaborative tasks improves with effective communication systems.
- Users prefer natural communication methods, even if they are less reliable.
- Combining predictive models with explicit communication yields the best results.
Computer Science > Robotics arXiv:2602.18850 (cs) [Submitted on 21 Feb 2026] Title:When the Inference Meets the Explicitness or Why Multimodality Can Make Us Forget About the Perfect Predictor Authors:J. E. Domínguez-Vidal, Alberto Sanfeliu View a PDF of the paper titled When the Inference Meets the Explicitness or Why Multimodality Can Make Us Forget About the Perfect Predictor, by J. E. Dom\'inguez-Vidal and 1 other authors View PDF HTML (experimental) Abstract:Although in the literature it is common to find predictors and inference systems that try to predict human intentions, the uncertainty of these models due to the randomness of human behavior has led some authors to start advocating the use of communication systems that explicitly elicit human intention. In this work, it is analyzed the use of four different communication systems with a human-robot collaborative object transportation task as experimental testbed: two intention predictors (one based on force prediction and another with an enhanced velocity prediction algorithm) and two explicit communication methods (a button interface and a voice-command recognition system). These systems were integrated into IVO, a custom mobile social robot equipped with force sensor to detect the force exchange between both agents and LiDAR to detect the environment. The collaborative task required transporting an object over a 5-7 meter distance with obstacles in the middle, demanding rapid decisions and precise physical coordi...
