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Found 16 papers, 9 papers with code
Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects
We address the problem of building digital twins of unknown articulated objects from two RGBD scans of the object at different articulation states.
Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces
Touch and vision go hand in hand, mutually enhancing our ability to understand the world.
Diff-DOPE: Differentiable Deep Object Pose Estimation
We introduce Diff-DOPE, a 6-DoF pose refiner that takes as input an image, a 3D textured model of an object, and an initial pose of the object.
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 #3 on
Robot Manipulation
on RLBench
Partial-View Object View Synthesis via Filtered Inversion
At inference, given one or more views of a novel real-world object, FINV first finds a set of latent codes for the object by inverting the generative model from multiple initial seeds.
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
Unlike previous unsupervised domain adaptation methods for category-level object pose estimation, our approach processes the test data in a sequential, online manner, and it does not require access to the source domain at runtime.
BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the object.
One-Shot Neural Fields for 3D Object Understanding
At test-time, we build the representation from a single RGB input image observing the scene from only one viewpoint.
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.
Correcting Robot Plans with Natural Language Feedback
In this paper, we explore natural language as an expressive and flexible tool for robot correction.
A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Execution
Natural language provides an accessible and expressive interface to specify long-term tasks for robotic agents.
Few-shot Object Grounding and Mapping for Natural Language Robot Instruction Following
We study the problem of learning a robot policy to follow natural language instructions that can be easily extended to reason about new objects.
Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight
Learning uses both simulation and real environments without requiring autonomous flight in the physical environment during training, and combines supervised learning for predicting positions to visit and reinforcement learning for continuous control.
Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction
We propose an approach for mapping natural language instructions and raw observations to continuous control of a quadcopter drone.
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
We propose to decompose instruction execution to goal prediction and action generation.
Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning
We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control.
