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09/14/2023
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects
Articulated object manipulation is a fundamental yet challenging task in...
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04/01/2023
JacobiNeRF: NeRF Shaping with Mutual Information Gradients
We propose a method that trains a neural radiance field (NeRF) to encode...
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11/01/2022
Seg Struct: The Interplay Between Part Segmentation and Structure Inference for 3D Shape Parsing
We propose Seg Struct, a supervised learning framework leveraging the ...
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10/04/2022
COPILOT: Human Collision Prediction and Localization from Multi-view Egocentric Videos
To produce safe human motions, assistive wearable exoskeletons must be e...
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07/05/2022
DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Object Manipulation
It is essential yet challenging for future home-assistant robots to unde...
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05/05/2022
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction
This paper studies the problem of fixing malfunctional 3D objects. While...
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04/20/2022
GIMO: Gaze-Informed Human Motion Prediction in Context
Predicting human motion is critical for assistive robots and AR/VR appli...
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12/19/2021
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment
Part assembly is a typical but challenging task in robotics, where robot...
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12/15/2021
Object Pursuit: Building a Space of Objects via Discriminative Weight Generation
We propose a framework to continuously learn object-centric representati...
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12/10/2021
IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes
Building embodied intelligent agents that can interact with 3D indoor en...
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12/01/2021
AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions
Perceiving and interacting with 3D articulated objects, such as cabinets...
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09/18/2021
Learning to Regrasp by Learning to Place
In this paper, we explore whether a robot can learn to regrasp a diverse...
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06/29/2021
O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning
Contrary to the vast literature in modeling, perceiving, and understandi...
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06/28/2021
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
Perceiving and manipulating 3D articulated objects (e.g., cabinets, door...
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12/04/2020
Compositionally Generalizable 3D Structure Prediction
Single-image 3D shape reconstruction is an important and long-standing p...
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08/12/2020
DSM-Net: Disentangled Structured Mesh Net for Controllable Generation of Fine Geometry
3D shape generation is a fundamental operation in computer graphics. Whi...
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06/14/2020
Generative 3D Part Assembly via Dynamic Graph Learning
Autonomous part assembly is a challenging yet crucial task in 3D compute...
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06/12/2020
Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks
In this paper, we examine the long-neglected yet important effects of po...
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03/21/2020
Learning 3D Part Assembly from a Single Image
Autonomous assembly is a crucial capability for robots in many applicati...
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03/19/2020
PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions
3D generative shape modeling is a fundamental research area in computer ...
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03/19/2020
SAPIEN: A SimulAted Part-based Interactive ENvironment
Building home assistant robots has long been a pursuit for vision and ro...
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02/16/2020
Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories
We address the problem of discovering 3D parts for objects in unseen cat...
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11/25/2019
StructEdit: Learning Structural Shape Variations
Learning to encode differences in the geometry and (topological) structu...
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08/01/2019
StructureNet: Hierarchical Graph Networks for 3D Shape Generation
The ability to generate novel, diverse, and realistic 3D shapes along wi...
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12/06/2018
PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding
We present PartNet: a consistent, large-scale dataset of 3D objects anno...
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02/24/2018
The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation
Deep reinforcement learning (DRL) demonstrates its potential in learning...
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12/02/2016
