arXiv subjects

Marco Controzzi

Publications and source records attributed to Marco Controzzi.

3 recordsLinked to original sources

arXiv2026

A Task-Space Receding Horizon Controller for Fast Collision Avoidance

Real-time collision avoidance for robotic manipulators requires fast reactions to unexpected obstacle motion and lookahead to avoid becoming trapped by near-future constraints. Full model predictive control can provide this foresight, but its online cost may grow quickly with horizon length, model fidelity, and the number of active geometric constraints. Conversely, horizon-free reactive methods are computationally efficient but can be short-sighted in dynamic clutter. We present a task-space receding-horizon controller that uses a short contact-consistent rollout to generate a terminal kinematic reference satisfying internal non-penetration constraints, then computes only the first input of a smooth minimum-acceleration transition toward that reference. Starting from a closed-loop inverse-kinematics regulation law, the rollout is performed with an iterative dynamics solver operating on inflated convex robot and obstacle geometries, so that robot-obstacle contacts, dynamic obstacle motion, and self-collisions can shape the terminal reference without requiring full constrained trajectory optimization. We analyze the contact-inactive closed loop and show local exponential task-space regulation under standard regularity assumptions. For contacts activated inside the rollout, we characterize the corresponding discrete updates and bound the effect of moving obstacles on regular operating sets. Simulations on a 40-DOF multi-chain system show that intermediate horizons balance anticipation, responsiveness, and computational cost. Hardware experiments on a 6-DOF platform demonstrate consistent sim-to-real behavior without accurate inertial parameter estimation, and comparisons against dynamic optimization fabrics and model predictive control (MPC) baselines show improved success rates in dynamic clutter while preserving solve times compatible with real-time execution in the tested regimes.

cs.RO↗

arXiv2026

The functional and temporal roles of gaze evolve across the phases and constraints of multi-stage robot-mediated manipulation

Goal-directed eye movements are a fundamental component of visuomotor control, enabling humans to anticipate and guide their actions. For this reason, they are increasingly used in human-robot interaction to estimate users' goals. However, during manipulation, fixations may reflect either an intended future action or the need to visually monitor the robotic proxy due to altered embodiment. How predictive and monitoring-related gaze are organized across the different phases of a constrained robot-mediated manipulation remains unclear. Here we address this question by investigating gaze behavior during goal-directed telemanipulation to characterize how visuomotor control adapts to altered embodiment in a multi-stage task. Our findings show that gaze remains strongly aligned with task goals, preserving its predictive role even during robot-mediated manipulation. At the same time, gaze frequently alternates between the robotic end-effector and the manipulated object, revealing increasing online monitoring. The presence and geometry of obstacles modulate the timing and distribution of these fixations, delaying attention to the final target when intermediate constraints become more demanding. These findings show that predictive gaze is not lost under altered embodiment but reorganized in response to changes in sensory feedback and control demands. More broadly, they highlight the flexibility of the human visuomotor system when the natural sensorimotor coupling is disrupted and suggest that gaze should be interpreted contextually rather than treating every fixation as direct evidence of user intention in human-robot interaction.

cs.RO↗

arXiv2022

A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations

Manipulating objects with robotic hands is a complicated task. Not only the fingers of the hand, but also the pose of the robot's end effector need to be coordinated. Using human demonstrations of movements is an intuitive and data-efficient way of guiding the robot's behavior. We propose a modular framework with an automatic embodiment mapping to transfer recorded human hand motions to robotic systems. In this work, we use motion capture to record human motion. We evaluate our approach on eight challenging tasks, in which a robotic hand needs to grasp and manipulate either deformable or small and fragile objects. We test a subset of trajectories in simulation and on a real robot and the overall success rates are aligned.

cs.RO↗