Valerio Belcamino

About

Valerio Belcamino is an emerging robotics researcher whose work sits at the dynamic intersection of human-robot collaboration, intent recognition, and motion planning. His research focuses on enabling robots to understand, anticipate, and seamlessly cooperate with human partners in real-world assembly and manipulation tasks. Among his most notable contributions is his 2024 work on gaze-based intention recognition, which has already garnered 8 citations, demonstrating that eye-tracking can serve as a powerful signal for robots to anticipate human intent during collaborative assembly scenarios. Complementing this, his modular planning framework leverages Hierarchical Task Networks to formalize complex cooperative tasks through compact sets of primitives, offering a flexible architecture for real-world human-robot teaming. Belcamino has also advanced the field of robot programming interfaces, evaluating augmented reality handheld tools for path specification, and contributed a multimodal dataset examining how object features influence human pick-and-place kinematics — a resource valuable to the broader learning-from-demonstration community. His investigations into dexterous in-hand manipulation further reflect his commitment to bridging human motor capabilities and robotic systems, making him a promising voice in next-generation collaborative robotics research.

Research Focus

Key Achievements

Top Papers

1. 1
   Gaze-Based Intention Recognition for Human-Robot Collaboration

   8 citations · 2024
2. 2
   Assessing the Effectiveness of Augmented Reality Handheld Interfaces for Robot Path Programming

   4 citations · 2022
3. 3
   The effects of selected object features on a pick-and-place task: A human multimodal dataset

   4 citations · 2023
4. 4
   A Modular Framework for Flexible Planning in Human-Robot Collaboration

   3 citations · 2024
5. 5
   In-hand manipulation planning using human motion dictionary

   2 citations · 2022

Key Collaborators

Contact & Links