Multi-Finger Grasping Like Humans
November 14, 2022 Β· Declared Dead Β· π IEEE/RJS International Conference on Intelligent RObots and Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yuming Du, Philippe Weinzaepfel, Vincent Lepetit, Romain BrΓ©gier
arXiv ID
2211.07304
Category
cs.RO: Robotics
Citations
7
Venue
IEEE/RJS International Conference on Intelligent RObots and Systems
Last Checked
4 months ago
Abstract
Robots with multi-fingered grippers could perform advanced manipulation tasks for us if we were able to properly specify to them what to do. In this study, we take a step in that direction by making a robot grasp an object like a grasping demonstration performed by a human. We propose a novel optimization-based approach for transferring human grasp demonstrations to any multi-fingered grippers, which produces robotic grasps that mimic the human hand orientation and the contact area with the object, while alleviating interpenetration. Extensive experiments with the Allegro and BarrettHand grippers show that our method leads to grasps more similar to the human demonstration than existing approaches, without requiring any gripper-specific tuning. We confirm these findings through a user study and validate the applicability of our approach on a real robot.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Robotics
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
π
π
The Cartographer
A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles
π
π
The Cartographer
Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges
π
π
The Cartographer
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
R.I.P.
π»
Ghosted
Learning agile and dynamic motor skills for legged robots
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted