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Found 19 papers, 9 papers with code
DexGarmentLab: Dexterous Garment Manipulation Environment with Generalizable Policy
To improve generalization across diverse garment shapes and deformations, we propose a Hierarchical gArment-manipuLation pOlicy (HALO).
Learning from Massive Human Videos for Universal Humanoid Pose Control
Scalable learning of humanoid robots is crucial for their deployment in real-world applications.
GAPartManip: A Large-scale Part-centric Dataset for Material-Agnostic Articulated Object Manipulation
To address these challenges, we introduced a large-scale part-centric dataset for articulated object manipulation that features both photo-realistic material randomization and detailed annotations of part-oriented, scene-level actionable interaction poses.
DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes
Grasping in cluttered scenes remains highly challenging for dexterous hands due to the scarcity of data.
Open6DOR: Benchmarking Open-instruction 6-DoF Object Rearrangement and A VLM-based Approach
In this work, we propel the pioneer construction of the benchmark and approach for table-top Open-instruction 6-DoF Object Rearrangement (Open6DOR).
Ranked #2 on
Object Rearrangement
on Open6DOR V2
PhysPart: Physically Plausible Part Completion for Interactable Objects
We also demonstrate our applications in 3D printing, robot manipulation, and sequential part generation, showing our strength in realistic tasks with the demand for high physical plausibility.
RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation
First, RAM extracts unified affordance at scale from diverse sources of demonstrations including robotic data, human-object interaction (HOI) data, and custom data to construct a comprehensive affordance memory.
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields
However, the permutation-equivariance requirement of MLFFs limits the design space of CG transform, that is, intensive CG transform has to be conducted for each neighboring edge and the operations should be performed in the same manner for all edges.
Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations
Autonomous robotic systems capable of learning novel manipulation tasks are poised to transform industries from manufacturing to service automation.
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.
Ranked #1 on
3D Question Answering (3D-QA)
on 3D MM-Vet
ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation
By fine-tuning the injected adapters, we preserve the inherent common sense and reasoning ability of the MLLMs while equipping them with the ability for manipulation.
SAGE: Bridging Semantic and Actionable Parts for GEneralizable Manipulation of Articulated Objects
More concretely, given an articulated object, we first observe all the semantic parts on it, conditioned on which an instruction interpreter proposes possible action programs that concretize the natural language instruction.
Make a Donut: Hierarchical EMD-Space Planning for Zero-Shot Deformable Manipulation with Tools
Remarkably, our model demonstrates robust generalization capabilities to novel and previously unencountered complex tasks without any preliminary demonstrations.
ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes
To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes.
UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning
We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud observations and proprioceptive information under a table-top setting, namely UniDexGrasp++.
PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations
Learning a generalizable object manipulation policy is vital for an embodied agent to work in complex real-world scenes.
UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy
Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud. For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution.
GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts
Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation.
End-to-End Affordance Learning for Robotic Manipulation
Such contact prediction process then leads to an end-to-end affordance learning framework that can generalize over different types of manipulation tasks.
