Ashok M. Sundaram — accepted AI-conference papers

2024

Bayesian Optimization for Robust Robotic Grasping Using a Sensorized Compliant Hand

Juan G. Lechuz-Sierra, Ana Elvira H. Martin, Ashok M. Sundaram, Ruben Martinez-Cantin, Máximo A. Roa

RA-L 2024

One of the first tasks we learn as children is to grasp objects based on our tactile perception. Incorporating such skill in robots will enable multiple applications, such as increasing flexibility in industrial processes or providing assistance to people with physical disabilities. However, the dif

Cited by 4 Source ↗Scholar ↗

2024

Unknown Object Grasping for Assistive Robotics

Elle Miller, Maximilian Durner, Matthias Humt, Gabriel Quere, Wout Boerdijk, Ashok M. Sundaram +2

ICRA 2024poster

We propose a novel pipeline for unknown object grasping in shared robotic autonomy scenarios. State-of-the-art methods for fully autonomous scenarios are typically learning-based approaches optimised for a specific end-effector, that generate grasp poses directly from sensor input. In the domain of…

Cited by 2 Source ↗Scholar ↗

2023

Multimodal Grasp Planner for Hybrid Grippers in Cluttered Scenes

Salvatore D'Avella, Ashok M. Sundaram, Werner Friedl, Paolo Tripicchio, Máximo A. Roa

RA-L 2023

Grasping a variety of objects is still an open problem in robotics, especially for cluttered scenarios. Multimodal grasping has been recognized as a promising strategy to improve the manipulation capabilities of a robotic system. This work presents a novel grasp planning algorithm for hybrid gripper

Cited by 30 Source ↗Scholar ↗

2023

Task-Oriented Stiffness Setting for a Variable Stiffness Hand

Ana Elvira H. Martin, Ashok M. Sundaram, Werner Friedl, Virginia Ruiz Garate, Máximo A. Roa

ICRA 2023poster

The integration of variable stiffness actuators (VSA) in robotic systems endows them with intrinsic flexibility and therefore robustness to unknown disturbances. However, this characteristic presents a challenge: choosing the best intrinsic stiffness setting guaranteeing the required force ap-plicat…

Cited by 5 Source ↗Scholar ↗

2020

A Bin-Picking Benchmark for Systematic Evaluation of Robotic Pick-and-Place Systems

Hussein Mnyusiwalla, Pavlos Triantafyllou, Panagiotis Sotiropoulos, Máximo A. Roa, Werner Friedl, Ashok M. Sundaram +2

RA-L 2020

Pick-and-place operations constitute the majority of today's industrial robotic applications. However, comparability and reproducibility of results has remained an issue that delays further advances in this field. Evaluation of manipulation systems can be carried out at different levels, but for the

Cited by 43 Source ↗Scholar ↗

2020

Environment-Aware Grasp Strategy Planning in Clutter for a Variable Stiffness Hand

Ashok M. Sundaram, Werner Friedl, Máximo A. Roa

IROS 2020poster

This paper deals with the problem of planning grasp strategies on constrained and cluttered scenarios. The planner sequences the objects for grasping by considering multiple factors: (i) possible environmental constraints that can be exploited to grasp an object, (ii) object neighborhood, (iii) capa…

Cited by 10 Source ↗Scholar ↗

2016

Grasp quality evaluation in underactuated robotic hands

Maria Pozzi, Ashok M. Sundaram, Monica Malvezzi, Domenico Prattichizzo, Máximo A. Roa

IROS 2016poster

Underactuated and synergy-driven hands are gaining attention in the grasping community mainly due to their simple kinematics, intrinsic compliance and versatility for grasping objects even in non structured scenarios. The evaluation of the grasping capabilities of such hands is a challenging task. T…

Cited by 13 Source ↗Scholar ↗