Search Results for author:
Found 18 papers, 11 papers with code
3D-MVP: 3D Multiview Pretraining for Robotic Manipulation
Our results suggest that 3D-aware pretraining is a promising approach to improve sample efficiency and generalization of vision-based robotic manipulation policies.
RVT-2: Learning Precise Manipulation from Few Demonstrations
In this work, we study how to build a robotic system that can solve multiple 3D manipulation tasks given language instructions.
Ranked #3 on
Robot Manipulation Generalization
on GEMBench
AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent
Encouraged by the remarkable achievements of language and vision foundation models, developing generalist robotic agents through imitation learning, using large demonstration datasets, has become a prominent area of interest in robot learning.
Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement
We propose a system for rearranging objects in a scene to achieve a desired object-scene placing relationship, such as a book inserted in an open slot of a bookshelf.
RVT: Robotic View Transformer for 3D Object Manipulation
In simulations, we find that a single RVT model works well across 18 RLBench tasks with 249 task variations, achieving 26% higher relative success than the existing state-of-the-art method (PerAct).
Ranked #6 on
Robot Manipulation
on RLBench
Infinite Photorealistic Worlds using Procedural Generation
We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world.
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
To ameliorate that effort, large language models (LLMs) can be used to score potential next actions during task planning, and even generate action sequences directly, given an instruction in natural language with no additional domain information.
Coupled Iterative Refinement for 6D Multi-Object Pose Estimation
We propose a new approach to 6D object pose estimation which consists of an end-to-end differentiable architecture that makes use of geometric knowledge.
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement
Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments.
Emotions are Subtle: Learning Sentiment Based Text Representations Using Contrastive Learning
Contrastive learning techniques have been widely used in the field of computer vision as a means of augmenting datasets.
Non-deep Networks
This begs the question -- is it possible to build high-performing "non-deep" neural networks?
Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline
It also outperforms state-of-the-art methods on ScanObjectNN, a real-world point cloud benchmark, and demonstrates better cross-dataset generalization.
Ranked #18 on
Point Cloud Classification
on PointCloud-C
Revisiting Point Cloud Classification with a Simple and Effective Baseline
It also outperforms state-of-the-art methods on ScanObjectNN, a real-world point cloud benchmark, and demonstrates better cross-dataset generalization.
Ranked #12 on
3D Point Cloud Classification
on ModelNet40-C
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D
The 3D scenes in our dataset come in minimally contrastive pairs: two scenes in a pair are almost identical, but a spatial relation holds in one and fails in the other.
Ranked #1 on
Spatial Relation Recognition
on Rel3D
PackIt: A Virtual Environment for Geometric Planning
The ability to jointly understand the geometry of objects and plan actions for manipulating them is crucial for intelligent agents.
Ranked #1 on
Robot Task Planning
on PackIt
Practical User Feedback-driven Internal Search Using Online Learning to Rank
We present a system, Spoke, for creating and searching internal knowledge base (KB) articles for organizations.
Think Visually: Question Answering through Virtual Imagery
In this paper, we study the problem of geometric reasoning in the context of question-answering.
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Such applications demand prediction models with small storage and computational complexity that do not compromise significantly on accuracy.
