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Rustam Stolkin

26 accepted papers

2026

GIFT: Geometry-Induced Functional Transfer for Category-Level Object Manipulation

Cristiana de Farias, Luis Figueredo, Riddhiman Laha, Komlan Jean Maxime Adjigble, Brahim Tamadazte, Rustam Stolkin +2
ICRA 2026poster

Robotic manipulation of unfamiliar objects in new environments is challenging due to limited generalisation capabilities. We propose a new skill transfer framework, GIFT (Geometry-Induced Functional Transfer), which enables a robot to transfer complex object manipulation skills and constraints from …

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Imitation learning for sim-to-real adaptation of robotic cutting policies based on residual Gaussian process disturbance force model

Jamie Hathaway, Rustam Stolkin, Alireza Rastegarpanah
IROS 2024poster

Robotic cutting, a crucial task in applications such as disassembly and decommissioning, faces challenges due to uncertainties in real-world environments. This paper presents a novel approach to enhance sim-to-real transfer of robotic cutting policies, leveraging a hybrid method integrating Gaussian…

Cited by 0Source ↗Scholar ↗
2024

Task-Informed Grasping of Partially Observed Objects

Cristiana de Farias, Brahim Tamadazte, Maxime Adjigble, Rustam Stolkin, Naresh Marturi
RA-L 2024

In this letter, we address the problem of task-informed grasping in scenarios where only incomplete or partial object information is available. Existing methods, which either focus on task-aware grasping or grasping under partiality, typically require extensive data and long training durations. In c

Cited by 2Source ↗Scholar ↗
2023

3D Spectral Domain Registration-Based Visual Servoing

Maxime Adjigble, Brahim Tamadazte, Cristiana de Farias, Rustam Stolkin, Naresh Marturi
ICRA 2023poster

This paper presents a spectral domain registration-based visual servoing scheme that works on 3D point clouds. Specifically, we propose a 3D model/point cloud alignment method, which works by finding a global transformation between reference and target point clouds using spectral analysis. A 3D Fast…

Cited by 4Source ↗Scholar ↗
2022

Grasp Transfer for Deformable Objects by Functional Map Correspondence

Cristiana De Farias, Brahim Tamadazte, Rustam Stolkin, Naresh Marturi
ICRA 2022poster

Handling object deformations for robotic grasping is still a major problem to solve. In this paper, we propose an efficient learning-free solution for this problem where generated grasp hypotheses of a region of an object are adapted to its deformed configurations. To this end, we investigate the ap…

Cited by 7Source ↗Scholar ↗
2022

Robot Vitals and Robot Health: Towards Systematically Quantifying Runtime Performance Degradation in Robots Under Adverse Conditions

Aniketh Ramesh, Rustam Stolkin, Manolis Chiou
RA-L 2022

This letter addresses the problem of automatically detecting and quantifying performance degradation in remote mobile robots, in real-time, during task execution. A robot may encounter a variety of uncertainties and adversities during task execution, which can impair its ability to carry out tasks e

Cited by 18Source ↗code ↗Scholar ↗
2022

Robot-Assisted Nuclear Disaster Response: Report and Insights from a Field Exercise

Manolis Chiou, Georgios-Theofanis Epsimos, Grigoris Nikolaou, Pantelis Pappas, Giannis Petousakis, Stefan Mühl +1
IROS 2022poster

This paper reports on insights by robotics researchers that participated in a 5-day robot-assisted nuclear disaster response field exercise conducted by Kerntechnische Hilfdienst GmbH (KHG) in Karlsruhe, Germany. The German nuclear industry established KHG to provide a robot-assisted emergency respo…

Cited by 43Source ↗Scholar ↗
2021

Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects

Cristiana de Farias, Naresh Marturi, Rustam Stolkin, Yasemin Bekiroglu
RA-L 2021

This letter addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an obs

Cited by 47Source ↗Scholar ↗
2021

SpectGRASP: Robotic Grasping by Spectral Correlation

Maxime Adjigble, Cristiana de Farias, Rustam Stolkin, Naresh Marturi
IROS 2021poster

This paper presents a spectral correlation-based method (SpectGRASP) for robotic grasping of arbitrarily shaped, unknown objects. Given a point cloud of an object, SpectGRASP extracts contact points on the object’s surface matching the hand configuration. It neither requires offline training nor a-p…

Cited by 8Source ↗Scholar ↗
2020

Benchmarking Protocol for Grasp Planning Algorithms

Yasemin Bekiroglu, Naresh Marturi, Máximo A. Roa, Komlan Jean Maxime Adjigble, Tommaso Pardi, Cindy Grimm +3
RA-L 2020

Numerous grasp planning algorithms have been proposed since the 1980s. The grasping literature has expanded rapidly in recent years, building on greatly improved vision systems and computing power. Methods have been proposed to plan stable grasps on known objects (exact 3D model is available), famil

Cited by 44Source ↗Scholar ↗
2020

Estimating An Object’s Inertial Parameters By Robotic Pushing: A Data-Driven Approach

Nikos Mavrakis, Amir M. Ghalamzan E., Rustam Stolkin
IROS 2020poster

Estimating the inertial properties of an object can make robotic manipulations more efficient, especially in extreme environments. This paper presents a novel method of estimating the 2D inertial parameters of an object, by having a robot applying a push on it. We draw inspiration from previous anal…

Cited by 15Source ↗Scholar ↗
2020

Path planning for mobile manipulator robots under non-holonomic and task constraints

Tommaso Pardi, Vamsikrishna Maddali, Valerio Ortenzi, Rustam Stolkin, Naresh Marturi
IROS 2020poster

This paper presents a path planner, which enables a nonholonomic mobile manipulator to move its end-effector on an observed surface with a constrained orientation, given start and destination points. A partial point cloud of the environment is captured using a vision-based sensor, but no prior knowl…

Cited by 21Source ↗Scholar ↗
2020

Planning Maximum-Manipulability Cutting Paths

Tommaso Pardi, Valerio Ortenzi, Colin Fairbairn, Tony Pipe, Amir Masoud Ghalamzan Esfahani, Rustam Stolkin
RA-L 2020

This letter presents a method for constrained motion planning from vision, which enables a robot to move its end-effector over an observed surface, given start and destination points. The robot has no prior knowledge of the surface shape, but observes it from a noisy point cloud. We consider the mul

Cited by 23Source ↗Scholar ↗
2019

An assisted telemanipulation approach: combining autonomous grasp planning with haptic cues

Maxime Adjigble, Naresh Marturi, Valerio Ortenzi, Rustam Stolkin
IROS 2019poster

This paper presents an assisted telemanipulation approach with integrated grasp planning. It also studies how the human teleoperation performance benefits from the incorporated visual and haptic cues while manipulating objects in cluttered environments. The developed system combines the widely used…

Cited by 17Source ↗Scholar ↗
2018

Learning Monocular Visual Odometry with Dense 3D Mapping from Dense 3D Flow

Cheng Zhao, Li Sun, Pulak Purkait, Tom Duckett, Rustam Stolkin
IROS 2018poster

This paper introduces a fully deep learning approach to monocular SLAM, which can perform simultaneous localization using a neural network for learning visual odometry (L-VO) and dense 3D mapping. Dense 2D flow and a depth image are generated from monocular images by sub-networks, which are then use…

Cited by 50Source ↗Scholar ↗
2018

Model-free and learning-free grasping by Local Contact Moment matching

Maxime Adjigble, Naresh Marturi, Valerio Ortenzi, Vijaykumar Rajasekaran, Peter Corke, Rustam Stolkin
IROS 2018poster

This paper addresses the problem of grasping arbitrarily shaped objects, observed as partial point-clouds, without requiring: models of the objects, physics parameters, training data, or other a-priori knowledge. A grasp metric is proposed based on Local Contact Moment (LoCoMo). LoCoMo combines zero…

Cited by 0Source ↗Scholar ↗
2017

Guiding Trajectory Optimization by Demonstrated Distributions

Takayuki Osa, Amir Masoud Ghalamzan Esfahani, Rustam Stolkin, Rudolf Lioutikov, Jan Peters, Gerhard Neumann
RA-L 2017

Trajectory optimization is an essential tool for motion planning under multiple constraints of robotic manipulators. Optimization-based methods can explicitly optimize a trajectory by leveraging prior knowledge of the system and have been used in various applications such as collision avoidance. How

Cited by 60Source ↗Scholar ↗
2017

Human-in-the-loop optimisation: Mixed initiative grasping for optimally facilitating post-grasp manipulative actions

E. Amir M. Ghalamzan, Firas Abi-Farraj, Paolo Robuffo Giordano, Rustam Stolkin
IROS 2017poster

This paper addresses the problem of mixed initiative, shared control for master-slave grasping and manipulation. We propose a novel system, in which an autonomous agent assists a human in teleoperating a remote slave arm/gripper, using a haptic master device. Our system is designed to exploit the hu…

Cited by 46Source ↗Scholar ↗
2017

Single-shot clothing category recognition in free-configurations with application to autonomous clothes sorting

Li Sun, Gerardo Aragon-Camarasa, Simon Rogers, Rustam Stolkin, J. Paul Siebert
IROS 2017poster

This paper proposes a single-shot approach for recognising clothing categories from 2.5D features. We propose two visual features, BSP (B-Spline Patch) and TSD (Topology Spatial Distances) for this task. The local BSP features are encoded by LLC (Locality-constrained Linear Coding) and fused with th…

Cited by 46Source ↗Scholar ↗
2016

Experimental analysis of a variable autonomy framework for controlling a remotely operating mobile robot

Manolis Chiou, Rustam Stolkin, Goda Bieksaite, Nick Hawes, Kimron L. Shapiro, Timothy S. Harrison
IROS 2016poster

This paper presents a principled experimental analysis of a variable autonomy control approach to mobile robot navigation. A Human-Initiative (HI) variable autonomy system is investigated, in which a human operator is able to switch the Level of Autonomy (LOA) between teleoperation (joystick control…

Cited by 42Source ↗Scholar ↗
2016

Task-relevant grasp selection: A joint solution to planning grasps and manipulative motion trajectories

Amir M. Ghalamzan E., Nikos Mavrakis, Marek Kopicki, Rustam Stolkin, Ales Leonardis
IROS 2016poster

This paper addresses the problem of jointly planning both grasps and subsequent manipulative actions. Previously, these two problems have typically been studied in isolation, however joint reasoning is essential to enable robots to complete real manipulative tasks. In this paper, the two problems ar…

Cited by 25Source ↗Scholar ↗
2016

Vision-guided state estimation and control of robotic manipulators which lack proprioceptive sensors

Valerio Ortenzi, Naresh Marturi, Rustam Stolkin, Jeffrey A. Kuo, Michael Mistry
IROS 2016poster

This paper presents a vision-based approach for estimating the configuration of, and providing control signals for, an under-sensored robot manipulator using a single monocular camera. Some remote manipulators, used for decommissioning tasks in the nuclear industry, lack proprioceptive sensors becau…

Cited by 17Source ↗Scholar ↗
2015

Projected inverse dynamics control and optimal control for robots in contact with the environment: A comparison

Valerio Ortenzi, Rustam Stolkin, Jeffrey A. Kuo, Michael Mistry
IROS 2015poster

This paper addresses the problem of constrained motion for a manipulator performing a task while in contact with the environment, and investigates two force control frameworks, one based on projected inverse dynamics, and one based on optimal control. Firstly, we propose a control method based on pr…

Cited by 9Source ↗Scholar ↗
2015

Single Target Tracking Using Adaptive Clustered Decision Trees and Dynamic Multi-Level Appearance Models

Jingjing Xiao, Rustam Stolkin, Ales Leonardis
CVPR 2015poster

This paper presents a method for single target tracking of arbitrary objects in challenging video sequences. Targets are modeled at three different levels of granularity (pixel level, parts-based level and bounding box level), which are cross-constrained to enable robust model relearning. The main c…

Cited by 71Source ↗PDF ↗Scholar ↗