89 citations · 156 across the 22 of their papers we have counts for
22 papers
Reflective Prompted Policy Optimization: Trajectory-Grounded Revision and Salience Bias
Rahaf Abu Hara, Vaibbhav Murarri, Claudio Zito
Existing LLM-based policy optimizers see only scalar rewards: that a policy scored 0.45, but not whether the agent got stuck in a loop, fell into a hole on the third step, or perfo…
LiZIP: An Auto-Regressive Compression Framework for LiDAR Point Clouds
Aditya Shibu, Kayvan Karim, Claudio Zito
The massive volume of data generated by LiDAR sensors in autonomous vehicles creates a bottleneck for real-time processing and vehicle-to-everything (V2X) transmission. Existing lo…
Robot Grasping and Manipulation: A Prospective
Claudio Zito
``A simple handshake would give them away''. This is how Anthony Hopkins' fictional character, Dr Robert Ford, summarises a particular flaw of the 2016 science-fiction \emph{Westwo…
Toward a Framework for Adaptive Impedance Control of an Upper-limb Prosthesis
Laura Ferrante, Mohan Sridharan, Claudio Zito +1
Adapting upper-limb impedance (i.e., stiffness, damping, inertia) is essential for humans interacting with dynamic environments for executing grasping or manipulation tasks. On the…
One-shot Learning for Autonomous Aerial Manipulation
Claudio Zito, Eliseo Ferrante
This paper is concerned with learning transferable contact models for aerial manipulation tasks. We investigate a contact-based approach for enabling unmanned aerial vehicles with…
Direct Mutation and Crossover in Genetic Algorithms Applied to Reinforcement Learning Tasks
Tarek Faycal, Claudio Zito
Neuroevolution has recently been shown to be quite competitive in reinforcement learning (RL) settings, and is able to alleviate some of the drawbacks of gradient-based approaches.…
Dyna-T: Dyna-Q and Upper Confidence Bounds Applied to Trees
Tarek Faycal, Claudio Zito
In this work we present a preliminary investigation of a novel algorithm called Dyna-T. In reinforcement learning (RL) a planning agent has its own representation of the environmen…
Multi-Hypothesis Scan Matching through Clustering
Giorgio Iavicoli, Claudio Zito
Graph-SLAM is a well-established algorithm for constructing a topological map of the environment while simultaneously attempting the localisation of the robot. It relies on scan ma…
Underwater Object Classification and Detection: first results and open challenges
Andre Jesus, Claudio Zito, Claudio Tortorici +2
This work reviews the problem of object detection in underwater environments. We analyse and quantify the shortcomings of conventional state-of-the-art (SOTA) algorithms in the com…
Learning Transferable Push Manipulation Skills in Novel Contexts
Rhys Howard, Claudio Zito
This paper is concerned with learning transferable forward models for push manipulation that can be applying to novel contexts and how to improve the quality of prediction when cri…
Statistical Context-Dependent Units Boundary Correction for Corpus-based Unit-Selection Text-to-Speech
Claudio Zito, Fabio Tesser, Mauro Nicolao +1
In this study, we present an innovative technique for speaker adaptation in order to improve the accuracy of segmentation with application to unit-selection Text-To-Speech (TTS) sy…
Aging Touch: Systematic and Unbiased Presentation of Tactile Stimuli
Claudio Zito
This report presents the experimental methodology and a step-by-step guide for gathering data on how aging influences tactile surface perception in decision and action. The experim…
Grasping and Manipulation with a Multi-Fingered Hand
Claudio Zito
This thesis is concerned with deriving planning algorithms for robot manipulators. Manipulation has two effects, the robot has a physical effect on the object, and it also acquires…
Multisensory Learning Framework for Robot Drumming
A. Barsky, C. Zito, H. Mori +2
The hype about sensorimotor learning is currently reaching high fever, thanks to the latest advancement in deep learning. In this paper, we present an open-source framework for col…
Robust and fast generation of top and side grasps for unknown objects
Brice Denoun, Beatriz Leon, Claudio Zito +3
In this work, we present a geometry-based grasping algorithm that is capable of efficiently generating both top and side grasps for unknown objects, using a single view RGB-D camer…
2D Linear Time-Variant Controller for Human's Intention Detection for Reach-to-Grasp Trajectories in Novel Scenes
Claudio Zito, Tomasz Deregowski, Rustam Stolkin
Designing robotic assistance devices for manipulation tasks is challenging. This work is concerned with improving accuracy and usability of semi-autonomous robots, such as human op…
Automatic Detection of Myocontrol Failures Based upon Situational Context Information
Karoline Heiwolt, Claudio Zito, Markus Nowak +2
Myoelectric control systems for assistive devices are still unreliable. The user's input signals can become unstable over time due to e.g. fatigue, electrode displacement, or sweat…
Generative grasp synthesis from demonstration using parametric mixtures
Ermano Arruda, Claudio Zito, Mohan Sridharan +2
We present a parametric formulation for learning generative models for grasp synthesis from a demonstration. We cast new light on this family of approaches, proposing a parametric…
Metrics and Benchmarks for Remote Shared Controllers in Industrial Applications
Claudio Zito, Maxime Adjigble, Brice D. Denoun +3
Remote manipulation is emerging as one of the key robotics tasks needed in extreme environments. Several researchers have investigated how to add AI components into shared controll…
Let's Push Things Forward: A Survey on Robot Pushing
Jochen Stüber, Claudio Zito, Rustam Stolkin
As robot make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable cir…
Feature-Based Transfer Learning for Robotic Push Manipulation
Jochen Stüber, Marek Kopicki, Claudio Zito
This paper presents a data-efficient approach to learning transferable forward models for robotic push manipulation. Our approach extends our previous work on contact-based predict…
Hypothesis-based Belief Planning for Dexterous Grasping
Claudio Zito, Valerio Ortenzi, Maxime Adjigble +3
Belief space planning is a viable alternative to formalise partially observable control problems and, in the recent years, its application to robot manipulation problems has grown.…