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20222024
most citedExploiting Kinematic Redundancy for Robotic Grasping of Multiple Objects

40 citations · 41 across the 5 of their papers we have counts for

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cs.RO20241 cited

Learning Dynamical Systems Encoding Non-Linearity within Space Curvature

Bernardo Fichera, Aude Billard

Dynamical Systems (DS) are an effective and powerful means of shaping high-level policies for robotics control. They provide robust and reactive control while ensuring the stabilit…

cs.RO2023

A Structured Prediction Approach for Robot Imitation Learning

Anqing Duan, Iason Batzianoulis, Raffaello Camoriano +3

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observe…

cs.RO202340 cited

Exploiting Kinematic Redundancy for Robotic Grasping of Multiple Objects

Kunpeng Yao, Aude Billard

Humans coordinate the abundant degrees of freedom (DoFs) of hands to dexterously perform tasks in everyday life. We imitate human strategies to advance the dexterity of multi-DoF r…

cs.RO2022

A Solution to Adaptive Mobile Manipulator Throwing

Yang Liu, Aradhana Nayak, Aude Billard

Mobile manipulator throwing is a promising method to increase the flexibility and efficiency of dynamic manipulation in factories. Its major challenge is to efficiently plan a feas…

cs.RO2022

Learning High Dimensional Demonstrations Using Laplacian Eigenmaps

Sthithpragya Gupta, Aradhana Nayak, Aude Billard

This article proposes a novel methodology to learn a stable robot control law driven by dynamical systems. The methodology requires a single demonstration and can deduce a stable d…