REVIEW 4 cited by
Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape Augmentation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Dexterous robotic hands have the capability to interact with a wide variety of household objects to perform tasks like grasping. However, learning robust real world grasping policies for arbitrary objects has proven challenging due to the difficulty of generating high quality training data. In this work, we propose a learning system (ISAGrasp) for leveraging a small number of human demonstrations to bootstrap the generation of a much larger dataset containing successful grasps on a variety of novel objects. Our key insight is to use a correspondence-aware implicit generative model to deform object meshes and demonstrated human grasps in order to generate a diverse dataset of novel objects and successful grasps for supervised learning, while maintaining semantic realism. We use this dataset to train a robust grasping policy in simulation which can be deployed in the real world. We demonstrate grasping performance with a four-fingered Allegro hand in both simulation and the real world, and show this method can handle entirely new semantic classes and achieve a 79% success rate on grasping unseen objects in the real world.
Forward citations
Cited by 4 Pith papers
-
DexVLG: Dexterous Vision-Language-Grasp Model at Scale
DexVLG is a vision-language model trained on 170 million simulated dexterous grasps that generates hand poses aligned with language instructions about which part of an object to grasp.
-
BODex: Scalable and Efficient Robotic Dexterous Grasp Synthesis Using Bilevel Optimization
A GPU-parallel bilevel optimization pipeline synthesizes high-quality dexterous grasps faster than prior methods and produces a dataset that improves learned grasping performance from about 40% to 80% in simulation.
-
Grasp What You Want: Embodied Dexterous Grasping System Driven by Your Voice
EDGS integrates vision-language enrichment with analytical dexterous grasp planning, reporting high success rates for voice-commanded grasping in cluttered real-world scenes.
-
A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
Embodied intelligence learning is reviewed through the complementary lenses of physical simulators and world models, with a proposed IR-L0 to IR-L4 robot capability taxonomy.
Discussion (0). Continue with ORCID to comment.