1,374 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
Learning Generalizable Dexterous Manipulation from Human Grasp Affordance
Dexterous manipulation with a multi-finger hand is one of the most
challenging problems in robotics. While recent progress in imitation learning
has largely improved the sample efficiency compared to Reinforcement Learning,
the learned policy can hardly generalize to manipulate novel objects, given
limited expert demonstrations. In this paper, we propose to learn dexterous
manipulation using large-scale demonstrations with diverse 3D objects in a
category, which are generated from a human grasp affordance model. This
generalizes the policy to novel object instances within the same category. To
train the policy, we propose a novel imitation learning objective jointly with
a geometric representation learning objective using our demonstrations. By
experimenting with relocating diverse objects in simulation, we show that our
approach outperforms baselines with a large margin when manipulating novel
objects. We also ablate the importance on 3D object representation learning for
manipulation. We include videos, code, and additional information on the
project website - https://kristery.github.io/ILAD/ .Comment: project page: https://kristery.github.io/ILAD
NASA space station automation: AI-based technology review
Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures
Plan-Guided Reinforcement Learning for Whole-Body Manipulation
Synthesizing complex whole-body manipulation behaviors has fundamental
challenges due to the rapidly growing combinatorics inherent to contact
interaction planning. While model-based methods have shown promising results in
solving long-horizon manipulation tasks, they often work under strict
assumptions, such as known model parameters, oracular observation of the
environment state, and simplified dynamics, resulting in plans that cannot
easily transfer to hardware. Learning-based approaches, such as imitation
learning (IL) and reinforcement learning (RL), have been shown to be robust
when operating over in-distribution states; however, they need heavy human
supervision. Specifically, model-free RL requires a tedious reward-shaping
process. IL methods, on the other hand, rely on human demonstrations that
involve advanced teleoperation methods. In this work, we propose a plan-guided
reinforcement learning (PGRL) framework to combine the advantages of
model-based planning and reinforcement learning. Our method requires minimal
human supervision because it relies on plans generated by model-based planners
to guide the exploration in RL. In exchange, RL derives a more robust policy
thanks to domain randomization. We test this approach on a whole-body
manipulation task on Punyo, an upper-body humanoid robot with compliant,
air-filled arm coverings, to pivot and lift a large box. Our preliminary
results indicate that the proposed methodology is promising to address
challenges that remain difficult for either model- or learning-based strategies
alone.Comment: 4 pages, 4 figure
Stabilize to Act: Learning to Coordinate for Bimanual Manipulation
Key to rich, dexterous manipulation in the real world is the ability to
coordinate control across two hands. However, while the promise afforded by
bimanual robotic systems is immense, constructing control policies for dual arm
autonomous systems brings inherent difficulties. One such difficulty is the
high-dimensionality of the bimanual action space, which adds complexity to both
model-based and data-driven methods. We counteract this challenge by drawing
inspiration from humans to propose a novel role assignment framework: a
stabilizing arm holds an object in place to simplify the environment while an
acting arm executes the task. We instantiate this framework with BimanUal
Dexterity from Stabilization (BUDS), which uses a learned restabilizing
classifier to alternate between updating a learned stabilization position to
keep the environment unchanged, and accomplishing the task with an acting
policy learned from demonstrations. We evaluate BUDS on four bimanual tasks of
varying complexities on real-world robots, such as zipping jackets and cutting
vegetables. Given only 20 demonstrations, BUDS achieves 76.9% task success
across our task suite, and generalizes to out-of-distribution objects within a
class with a 52.7% success rate. BUDS is 56.0% more successful than an
unstructured baseline that instead learns a BC stabilizing policy due to the
precision required of these complex tasks. Supplementary material and videos
can be found at https://sites.google.com/view/stabilizetoact .Comment: Conference on Robot Learning, 202
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