Dexterous manipulation using both palm and fingers

By Yunfei Bai, C. Karen Liu. 2014

138 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Solving Rubik's Cube with a Robot Hand (2019, methodology): “[6] propose methods to generate trajectories for complex and dynamic in-hand manipulation, but their results are limited to simulation.”
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations (2017, background): “However, these methods often require detailed models and careful attention to details by an expert [4].”
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost (2018, background): “In simulation, [10], [11], [12] have shown the ability to synthesize complex behaviors when provided with a ground truth model.”
Learning dexterous in-hand manipulation (2018, neutral): “Mordatch et al. [40] and Bai et al. [5] propose methods to generate trajectories for complex and dynamic in-hand manipulation, but results are limited to simulation.”
Deep Dynamics Models for Learning Dexterous Manipulation (2019, neutral): “Prior work in this area has explored a wide spectrum of manipulation strategies: [8]’s optimizer used geometric meshes of the object to plan finger-gaiting maneuvers, [9]’s multi-phase planner used the help of offline simulations and a reduced basis of hand poses to accelerate the parameter search f”
Learning dexterous manipulation for a soft robotic hand from human demonstrations (2016, background): “There exist planners that integrate contact kinematics, non-holonomic motion constraints, and grasp stability to come up with manipulation plans based on ﬁnger gaits [5], rolling and sliding ﬁngertip motions [6], or nonprehensile actions involving only the palm [7].”
Learning robot in-hand manipulation with tactile features (2015, methodology): “Such approaches have been used successfully for finger gaiting [2], manipulation with a rolling contact [2–5], sliding [5], in-grasp-manipulation [6], and rolling objects over the hand palm [7].”
DexMV: Imitation Learning for Dexterous Manipulation from Human Videos (2021, background): “Manipulation with multifinger hands is one of the most challenging core robotics tasks, which has been actively studied with optimization and planning [63, 9, 46, 18, 2, 6].”
Learning Dexterous In-Hand Manipulation. (2018, methodology): “Some prior methods have shown promising in-hand manipulation results in simulation but do not attempt to transfer to a real world robot [5, 40].”
A System for General In-Hand Object Re-Orientation (2021, methodology): “Past work has tackled the in-hand reorientation problem via several approaches: (i) The use of analytical models with powerful trajectory optimization methods [1, 2, 3].”
Demonstration-Guided Deep Reinforcement Learning of Control Policies for Dexterous Human-Robot Interaction (2019, methodology): “Most approaches therefore use trajectory optimization to provide a controller [3], [16], [17].”
Learning to Solve a Rubik’s Cube with a Dexterous Hand (2019, background): “Traditional approaches [1], [2] studied simple tasks including grasping and picking up objects in simulation.”
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation (2017, unknown): “Both geometry-based [13], [14], [15] and data-driven methods [16] have been proposed to address the problem of robotic grasping.”
On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation (2018, background): “Many works, such as [9]– [12], use motion planning and control techniques to carry out dexterous manipulation tasks.”
Learning Dexterous Grasping with Object-Centric Visual Affordances (2020, methodology): “explores related open-loop strategies with complex controllers, but, unlike our method, assumes access to the full 3D model of the objects [10], [2], [4], [7], [44].”