Valerio Bo — accepted AI-conference papers

2026

Leveraging Embodied Mechanical Intelligence for Learning Decluttering Tasks

Enrico Turco, Valerio Bo, Chiara Castellani, Gionata Salvietti, Monica Malvezzi, Domenico Prattichizzo +1

ICRA 2026poster

In this work, we investigate how a state-of-the-art grasp planner based on deep reinforcement learning performs when applied to a soft-rigid gripper in a decluttering task. The gripper, called Soft ScoopGripper, is endowed with a rigid scoop-shaped part that facilitates the interaction with the envi…

Cited by 0 Scholar ↗

2026

Visual Proactivity: Enhancing Human-Robot Collaboration through Intent Communication

Valerio Bo, Edison Bejarano, Anais Garrell, Alberto Sanfeliu

ICRA 2026poster

As robots transition from performing repetitive tasks to collaborating with humans, understanding human intent becomes crucial to effective interaction. Anticipation enables robots to predict human actions, while proactivity allows them to take initiative and guide human behavior toward optimal outc…

Cited by 0 Scholar ↗

2025

Soft Human-Robot Handover Using a Vision-Based Pipeline

Chiara Castellani, Enrico Turco, Valerio Bo, Monica Malvezzi, Domenico Prattichizzo, Gabriele Costante +1

RA-L 2025

Handing over objects is an essential task in human-robot collaborative scenarios. Previous studies have predominantly employed rigid grippers to perform the handover, focusing on generating grasps that avoid physical contact with people. In this paper, we present a vision-based open-palm handover so

Cited by 7 Source ↗Scholar ↗

2024

Reducing Cognitive Load in Teleoperating Swarms of Robots through a Data-Driven Shared Control Approach

Enrico Turco, Chiara Castellani, Valerio Bo, Claudio Pacchierotti, Domenico Prattichizzo, Tommaso Lisini Baldi

IROS 2024poster

Multi-robot systems have gained increasing interest across various fields such as medicine, environmental monitoring, and more. Despite the evident advantages, the coordination of the swarm arises significant challenges for human operators, particularly concerning the cognitive burden needed for eff…

Cited by 3 Source ↗Scholar ↗

2024

The Double-Scoop Gripper: A Tendon-Driven Soft-Rigid End-Effector for Food Handling Exploiting Constraints in Narrow Spaces

Leonardo Franco, Enrico Turco, Valerio Bo, Maria Pozzi, Monica Malvezzi, Domenico Prattichizzo +1

ICRA 2024poster

Food handling is a challenging task for robotic grippers, as it requires to manipulate highly deformable and fragile items, that can be easily damaged. Moreover, ingredients for the preparation of the different dishes are usually stored in small containers that are often not easily accessible. This…

Cited by 1 Source ↗Scholar ↗

2022

Automated Design of Embedded Constraints for Soft Hands Enabling New Grasp Strategies

Valerio Bo, Enrico Turco, Maria Pozzi, Monica Malvezzi, Domenico Prattichizzo

RA-L 2022

Soft robotic hands allow to fully exploit hand-object-environment interactions to complete grasping tasks. However, their usability can still be limited in some scenarios (e.g., restricted or cluttered spaces). In this article, we propose to enhance the versatility of soft grippers by adding special

Cited by 9 Source ↗Scholar ↗

2021

Grasp Planning With a Soft Reconfigurable Gripper Exploiting Embedded and Environmental Constraints

Enrico Turco, Valerio Bo, Maria Pozzi, Alessandro Rizzo, Domenico Prattichizzo

RA-L 2021

Grasping in unstructured environments requires highly adaptable and versatile hands together with strategies to exploit their features to get robust grasps. This letter presents a method to grasp objects using a novel reconfigurable soft gripper with embodied constraints, the Soft ScoopGripper (SSG)

Cited by 19 Source ↗Scholar ↗