Loading [MathJax]/extensions/MathMenu.js
Bipul C. Paul - IEEE Xplore Author Profile
Subscribe
ADVANCED SEARCH

Author details

< Back

Bipul C. Paul

Also published under: Bipul Paul, B. C. Paul, B. Paul

Publications
51
Citations
2,513
Publications by Year
20002010
Co-Authors:
Show All Co-Authors (45)
Image of author Bipul C. Paul

Bipul C. Paul

Also published under: Bipul Paul, B. C. Paul, B. Paul

Affiliation

Toshiba America Research
Inc.
San Jose, CA, USA

Publication Topics

Biography

Bipul C. Paul (S'97–M'01–SM'05) received the B.Tech. and M.Tech. degrees in radiophysics and electronics from the University of Calcutta, Calcutta, India, and the Ph.D. degree from Indian Institute of Science (IISc), Bangalore, India.,He is currently a Senior Research Scientist with Toshiba America Research, San Jose, CA. Earlier, he was with Alliance Semiconductor (India), where he worked on synchronous DRAM design. From 2000 to 2005, he was with Purdue University, West Lafayette, IN, as a Postdoctoral Research Associate, where he developed several device models and statistical design techniques for nanoscale circuits and memory. He has also developed several device and circuit optimization techniques for ultralow-power digital subthreshol...
Publications
51
Citations
2,513
Publications by Year
20002010
Co-Authors:
Show All Co-Authors (45)
Author's Published Works

Showing 1-14 of 14 results

  • Sort

Filter Results

Show

Sort By

Results

Resource-Efficient Personalization in Federated Learning With Closed-Form Classifiers

Eros Fanì;Raffaello Camoriano;Barbara Caputo;Marco Ciccone

IEEE Access
Year: 2025 | Volume: 13 | Journal Article |
Statistical heterogeneity in Federated Learning (FL) often leads to client drift and biased local solutions. Prior work in the literature shows that client drift particularly affects the parameters of the classification layer, hindering both convergence and accuracy. While Personalized FL (PFL) addresses this by allowing client-specific models, it can overlook valuable global knowledge. This paper...Show More

Resource-Efficient Personalization in Federated Learning With Closed-Form Classifiers

Eros Fanì;Raffaello Camoriano;Barbara Caputo;Marco Ciccone

Year: 2025 | Volume: 13 | Journal Article |

Learning Rhythmic Trajectories With Geometric Constraints for Laser-Based Skincare Procedures

Anqing Duan;Wanli Liuchen;Jinsong Wu;Raffaello Camoriano;Lorenzo Rosasco;David Navarro-Alarcon

IEEE Transactions on Robotics
Year: 2025 | Volume: 41 | Journal Article |
The increasing deployment of robots has significantly enhanced the automation levels across a wide and diverse range of industries. This article investigates the automation challenges of laser-based dermatology procedures in the beauty industry. This group of related manipulation tasks involves delivering energy from a cosmetic laser onto the skin with repetitive patterns. To automate this procedu...Show More

Learning Rhythmic Trajectories With Geometric Constraints for Laser-Based Skincare Procedures

Anqing Duan;Wanli Liuchen;Jinsong Wu;Raffaello Camoriano;Lorenzo Rosasco;David Navarro-Alarcon

Year: 2025 | Volume: 41 | Journal Article |

Long-Term Upper-Limb Prosthesis Myocontrol via High-Density sEMG and Incremental Learning

Dario Di Domenico;Nicolò Boccardo;Andrea Marinelli;Michele Canepa;Emanuele Gruppioni;Matteo Laffranchi;Raffaello Camoriano

IEEE Robotics and Automation Letters
Year: 2024 | Volume: 9, Issue: 11 | Journal Article |
Cited by: Papers (1)
Noninvasive human-machine interfaces such as surface electromyography (sEMG) have long been employed for controlling robotic prostheses. However, classical controllers are limited to few degrees of freedom (DoF). More recently, machine learning methods have been proposed to learn personalized controllers from user data. While promising, they often suffer from distribution shift during long-term us...Show More

Long-Term Upper-Limb Prosthesis Myocontrol via High-Density sEMG and Incremental Learning

Dario Di Domenico;Nicolò Boccardo;Andrea Marinelli;Michele Canepa;Emanuele Gruppioni;Matteo Laffranchi;Raffaello Camoriano

Year: 2024 | Volume: 9, Issue: 11 | Journal Article |

PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting

Gabriele Tiboni;Raffaello Camoriano;Tatiana Tommasi

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Popular industrial robotic problems such as spray painting and welding require (i) conditioning on free-shape 3D objects and (ii) planning of multiple trajectories to solve the task. Yet, existing solutions make strong assumptions on the form of input surfaces and the nature of output paths, resulting in limited approaches unable to cope with real-data variability. By leveraging on recent advances...Show More

PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting

Gabriele Tiboni;Raffaello Camoriano;Tatiana Tommasi

Year: 2023 | Conference Paper |

From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach

Elisa Maiettini;Andrea Maracani;Raffaello Camoriano;Giulia Pasquale;Vadim Tikhanoff;Lorenzo Rosasco;Lorenzo Natale

2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)
Year: 2022 | Conference Paper |
Deep Learning (DL) based methods for object detection achieve remarkable performance at the cost of computationally expensive training and extensive data labeling. Robots embodiment can be exploited to mitigate this burden by acquiring automatically annotated training data via a natural interaction with a human showing the object of interest, hand-held. However, learning solely from this data may ...Show More

From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach

Elisa Maiettini;Andrea Maracani;Raffaello Camoriano;Giulia Pasquale;Vadim Tikhanoff;Lorenzo Rosasco;Lorenzo Natale

Year: 2022 | Conference Paper |

ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots

Paolo Maria Viceconte;Raffaello Camoriano;Giulio Romualdi;Diego Ferigo;Stefano Dafarra;Silvio Traversaro;Giuseppe Oriolo;Lorenzo Rosasco;Daniele Pucci

IEEE Robotics and Automation Letters
Year: 2022 | Volume: 7, Issue: 2 | Journal Article |
Cited by: Papers (13)
Human-like trajectory generation and footstep planning represent challenging problems in humanoid robotics. Recently, research in computer graphics investigated machine-learning methods for character animation based on training human-like models directly on motion capture data. Such methods proved effective in virtual environments, mainly focusing on trajectory visualization. This letter presents ...Show More

ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots

Paolo Maria Viceconte;Raffaello Camoriano;Giulio Romualdi;Diego Ferigo;Stefano Dafarra;Silvio Traversaro;Giuseppe Oriolo;Lorenzo Rosasco;Daniele Pucci

Year: 2022 | Volume: 7, Issue: 2 | Journal Article |

On the Emergence of Whole-Body Strategies From Humanoid Robot Push-Recovery Learning

Diego Ferigo;Raffaello Camoriano;Paolo Maria Viceconte;Daniele Calandriello;Silvio Traversaro;Lorenzo Rosasco;Daniele Pucci

IEEE Robotics and Automation Letters
Year: 2021 | Volume: 6, Issue: 4 | Journal Article |
Cited by: Papers (10)
Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks. In this context, classical control systems tend to be based on simplified physical models and hard-coded strategies. Although successful in specific scenarios, this approach requires demanding tuning of parameters and switching logic between specifically-designed controllers for handl...Show More

On the Emergence of Whole-Body Strategies From Humanoid Robot Push-Recovery Learning

Diego Ferigo;Raffaello Camoriano;Paolo Maria Viceconte;Daniele Calandriello;Silvio Traversaro;Lorenzo Rosasco;Daniele Pucci

Year: 2021 | Volume: 6, Issue: 4 | Journal Article |

Structured Prediction for CRiSP Inverse Kinematics Learning With Misspecified Robot Models

Gian Maria Marconi;Raffaello Camoriano;Lorenzo Rosasco;Carlo Ciliberto

IEEE Robotics and Automation Letters
Year: 2021 | Volume: 6, Issue: 3 | Journal Article |
Cited by: Papers (3)
With the recent advances in machine learning, problems that traditionally would require accurate modeling to be solved analytically can now be successfully approached with data-driven strategies. Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinemat...Show More

Structured Prediction for CRiSP Inverse Kinematics Learning With Misspecified Robot Models

Gian Maria Marconi;Raffaello Camoriano;Lorenzo Rosasco;Carlo Ciliberto

Year: 2021 | Volume: 6, Issue: 3 | Journal Article |

Derivative-Free Online Learning of Inverse Dynamics Models

Diego Romeres;Mattia Zorzi;Raffaello Camoriano;Silvio Traversaro;Alessandro Chiuso

IEEE Transactions on Control Systems Technology
Year: 2020 | Volume: 28, Issue: 3 | Journal Article |
Cited by: Papers (21)
This paper discusses online algorithms for inverse dynamics modeling in robotics. Several model classes, including rigid body dynamics models, data-driven models and semiparametric models (which are combination of the previous two classes), are placed in a common framework. While model classes used in the literature typically exploit joint velocities and accelerations, which needs to be approximat...Show More

Derivative-Free Online Learning of Inverse Dynamics Models

Diego Romeres;Mattia Zorzi;Raffaello Camoriano;Silvio Traversaro;Alessandro Chiuso

Year: 2020 | Volume: 28, Issue: 3 | Journal Article |

Learning to Sequence Multiple Tasks with Competing Constraints

Anqing Duan;Raffaello Camoriano;Diego Ferigo;Yanlong Huang;Daniele Calandriello;Lorenzo Rosasco;Daniele Pucci

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2019 | Conference Paper |
Cited by: Papers (7)
Imitation learning offers a general framework where robots can efficiently acquire novel motor skills from demonstrations of a human teacher. While many promising achievements have been shown, the majority of them are only focused on single-stroke movements, without taking into account the problem of multi-tasks sequencing. Conceivably, sequencing different atomic tasks can further augment the rob...Show More

Learning to Sequence Multiple Tasks with Competing Constraints

Anqing Duan;Raffaello Camoriano;Diego Ferigo;Yanlong Huang;Daniele Calandriello;Lorenzo Rosasco;Daniele Pucci

Year: 2019 | Conference Paper |

Constrained DMPs for Feasible Skill Learning on Humanoid Robots

Anqing Duan;Raffaello Camoriano;Diego Ferigo;Daniele Calandriello;Lorenzo Rosasco;Daniele Pucci

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)
Year: 2018 | Conference Paper |
Cited by: Papers (15)
In the context of humanoid skill learning, movement primitives have gained much attention because of their compact representation and convenient combination with a myriad of optimization approaches. Among them, a well-known scheme is to use Dynamic Movement Primitives (DMPs) with reinforcement learning (RL) algorithms. While various remarkable results have been reported, skill learning with physic...Show More

Constrained DMPs for Feasible Skill Learning on Humanoid Robots

Anqing Duan;Raffaello Camoriano;Diego Ferigo;Daniele Calandriello;Lorenzo Rosasco;Daniele Pucci

Year: 2018 | Conference Paper |

Incremental robot learning of new objects with fixed update time

Raffaello Camoriano;Giulia Pasquale;Carlo Ciliberto;Lorenzo Natale;Lorenzo Rosasco;Giorgio Metta

2017 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2017 | Conference Paper |
Cited by: Papers (23)
We consider object recognition in the context of lifelong learning, where a robotic agent learns to discriminate between a growing number of object classes as it accumulates experience about the environment. We propose an incremental variant of the Regularized Least Squares for Classification (RLSC) algorithm, and exploit its structure to seamlessly add new classes to the learned model. The presen...Show More

Incremental robot learning of new objects with fixed update time

Raffaello Camoriano;Giulia Pasquale;Carlo Ciliberto;Lorenzo Natale;Lorenzo Rosasco;Giorgio Metta

Year: 2017 | Conference Paper |

Online semi-parametric learning for inverse dynamics modeling

Diego Romeres;Mattia Zorzi;Raffaello Camoriano;Alessandro Chiuso

2016 IEEE 55th Conference on Decision and Control (CDC)
Year: 2016 | Conference Paper |
Cited by: Papers (30)
This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model. It combines the strength of the parametric and non-parametric modeling. The former exploits the rigid body dynamics equation, while the latter exploits a suitable kernel function. We provide an extensive comparison with other methods from the literature using real data from the iCub humanoid robo...Show More

Online semi-parametric learning for inverse dynamics modeling

Diego Romeres;Mattia Zorzi;Raffaello Camoriano;Alessandro Chiuso

Year: 2016 | Conference Paper |

Incremental semiparametric inverse dynamics learning

Raffaello Camoriano;Silvio Traversaro;Lorenzo Rosasco;Giorgio Metta;Francesco Nori

2016 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2016 | Conference Paper |
Cited by: Papers (32)
This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. The result is an incremental semiparametric approach, ...Show More

Incremental semiparametric inverse dynamics learning

Raffaello Camoriano;Silvio Traversaro;Lorenzo Rosasco;Giorgio Metta;Francesco Nori

Year: 2016 | Conference Paper |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.