304 research outputs found
Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations
We propose to learn to generate grasping motion for manipulation with a
dexterous hand using implicit functions. With continuous time inputs, the model
can generate a continuous and smooth grasping plan. We name the proposed model
Continuous Grasping Function (CGF). CGF is learned via generative modeling with
a Conditional Variational Autoencoder using 3D human demonstrations. We will
first convert the large-scale human-object interaction trajectories to robot
demonstrations via motion retargeting, and then use these demonstrations to
train CGF. During inference, we perform sampling with CGF to generate different
grasping plans in the simulator and select the successful ones to transfer to
the real robot. By training on diverse human data, our CGF allows
generalization to manipulate multiple objects. Compared to previous planning
algorithms, CGF is more efficient and achieves significant improvement on
success rate when transferred to grasping with the real Allegro Hand. Our
project page is at https://jianglongye.com/cgf .Comment: Project page: https://jianglongye.com/cg
In-Hand Object Rotation via Rapid Motor Adaptation
Generalized in-hand manipulation has long been an unsolved challenge of
robotics. As a small step towards this grand goal, we demonstrate how to design
and learn a simple adaptive controller to achieve in-hand object rotation using
only fingertips. The controller is trained entirely in simulation on only
cylindrical objects, which then - without any fine-tuning - can be directly
deployed to a real robot hand to rotate dozens of objects with diverse sizes,
shapes, and weights over the z-axis. This is achieved via rapid online
adaptation of the controller to the object properties using only proprioception
history. Furthermore, natural and stable finger gaits automatically emerge from
training the control policy via reinforcement learning. Code and more videos
are available at https://haozhi.io/horaComment: CoRL 2022. Code and Website: https://haozhi.io/hor
General In-Hand Object Rotation with Vision and Touch
We introduce RotateIt, a system that enables fingertip-based object rotation
along multiple axes by leveraging multimodal sensory inputs. Our system is
trained in simulation, where it has access to ground-truth object shapes and
physical properties. Then we distill it to operate on realistic yet noisy
simulated visuotactile and proprioceptive sensory inputs. These multimodal
inputs are fused via a visuotactile transformer, enabling online inference of
object shapes and physical properties during deployment. We show significant
performance improvements over prior methods and the importance of visual and
tactile sensing.Comment: CoRL 2023; Website: https://haozhi.io/rotateit
Grasp Multiple Objects with One Hand
The human hand's complex kinematics allow for simultaneous grasping and
manipulation of multiple objects, essential for tasks like object transfer and
in-hand manipulation. Despite its importance, robotic multi-object grasping
remains underexplored and presents challenges in kinematics, dynamics, and
object configurations. This paper introduces MultiGrasp, a two-stage method for
multi-object grasping on a tabletop with a multi-finger dexterous hand. It
involves (i) generating pre-grasp proposals and (ii) executing the grasp and
lifting the objects. Experimental results primarily focus on dual-object
grasping and report a 44.13% success rate, showcasing adaptability to unseen
object configurations and imprecise grasps. The framework also demonstrates the
capability to grasp more than two objects, albeit at a reduced inference speed
Extrinsic Dexterity: In-Hand Manipulation with External Forces
Abstract — “In-hand manipulation ” is the ability to reposition an object in the hand, for example when adjusting the grasp of a hammer before hammering a nail. The common approach to in-hand manipulation with robotic hands, known as dexterous manipulation [1], is to hold an object within the fingertips of the hand and wiggle the fingers, or walk them along the object’s surface. Dexterous manipulation, however, is just one of the many techniques available to the robot. The robot can also roll the object in the hand by using gravity, or adjust the object’s pose by pressing it against a surface, or if fast enough, it can even toss the object in the air and catch it in a different pose. All these techniques have one thing in common: they rely on resources extrinsic to the hand, either gravity, external contacts or dynamic arm motions. We refer to them as “extrinsic dexterity”. In this paper we study extrinsic dexterity in the context of regrasp operations, for example when switching from a power to a precision grasp, and we demonstrate that even simple grippers are capable of ample in-hand manipulation. We develop twelve regrasp actions, all open-loop and handscripted, and evaluate their effectiveness with over 1200 trials of regrasps and sequences of regrasps, for three different objects (see video [2]). The long-term goal of this work is to develop a general repertoire of these behaviors, and to understand how such a repertoire might eventually constitute a general-purpose in-hand manipulation capability. I
Progressive Transfer Learning for Dexterous In-Hand Manipulation with Multi-Fingered Anthropomorphic Hand
Dexterous in-hand manipulation for a multi-fingered anthropomorphic hand is
extremely difficult because of the high-dimensional state and action spaces,
rich contact patterns between the fingers and objects. Even though deep
reinforcement learning has made moderate progress and demonstrated its strong
potential for manipulation, it is still faced with certain challenges, such as
large-scale data collection and high sample complexity. Especially, for some
slight change scenes, it always needs to re-collect vast amounts of data and
carry out numerous iterations of fine-tuning. Remarkably, humans can quickly
transfer learned manipulation skills to different scenarios with little
supervision. Inspired by human flexible transfer learning capability, we
propose a novel dexterous in-hand manipulation progressive transfer learning
framework (PTL) based on efficiently utilizing the collected trajectories and
the source-trained dynamics model. This framework adopts progressive neural
networks for dynamics model transfer learning on samples selected by a new
samples selection method based on dynamics properties, rewards and scores of
the trajectories. Experimental results on contact-rich anthropomorphic hand
manipulation tasks show that our method can efficiently and effectively learn
in-hand manipulation skills with a few online attempts and adjustment learning
under the new scene. Compared to learning from scratch, our method can reduce
training time costs by 95%.Comment: 12 pages, 7 figures, submitted to TNNL
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