Search Results for author:
Found 27 papers, 10 papers with code
OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data
This work presents OVIR-3D, a straightforward yet effective method for open-vocabulary 3D object instance retrieval without using any 3D data for training.
Ranked #4 on
3D Open-Vocabulary Instance Segmentation
on Replica
Socially Cognizant Robotics for a Technology Enhanced Society
Emerging applications of robotics, and concerns about their impact, require the research community to put human-centric objectives front-and-center.
Pick Planning Strategies for Large-Scale Package Manipulation
Automating warehouse operations can reduce logistics overhead costs, ultimately driving down the final price for consumers, increasing the speed of delivery, and enhancing the resiliency to market fluctuations.
Demonstrating Large-Scale Package Manipulation via Learned Metrics of Pick Success
This paper demonstrates a large-scale package manipulation from unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet, which utilizes a pick success predictor trained on real production data.
Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos
This work proposes a self-supervised learning system for segmenting rigid objects in RGB images.
Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine
This strategy is based on a differentiable physics engine that can be trained given limited data from a real robot.
6N-DoF Pose Tracking for Tensegrity Robots
To ensure that the pose estimates of rigid elements are physically feasible, i. e., they are not resulting in collisions between rods or with the environment, physical constraints are introduced during the optimization.
Lazy Rearrangement Planning in Confined Spaces
Object rearrangement is important for many applications but remains challenging, especially in confined spaces, such as shelves, where objects cannot be accessed from above and they block reachability to each other.
Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations
This work aims to learn how to perform complex robot manipulation tasks that are composed of several, consecutively executed low-level sub-tasks, given as input a few visual demonstrations of the tasks performed by a person.
A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data
A model of NASA's icosahedron SUPERballBot on MuJoCo is used as the ground truth system to collect training data.
You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration
The canonical object representation is learned solely in simulation and then used to parse a category-level, task trajectory from a single demonstration video.
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
This work proposes a framework to learn task-relevant grasping for industrial objects without the need of time-consuming real-world data collection or manual annotation.
BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models
Most prior efforts, however, often assume that the target object's CAD model, at least at a category-level, is available for offline training or during online template matching.
Ranked #1 on
6D Pose Estimation using RGBD
on REAL275
(mAP 3DIou@25 metric)
Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks
Highly constrained manipulation tasks continue to be challenging for autonomous robots as they require high levels of precision, typically less than 1mm, which is often incompatible with what can be achieved by traditional perception systems.
Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
This work presents se(3)-TrackNet, a data-driven optimization approach for long term, 6D pose tracking.
Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop
This report presents the debates, posters, and discussions of the Sim2Real workshop held in conjunction with the 2020 edition of the "Robotics: Science and System" conference.
Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots
The results indicate that only 0. 25\% of ground truth data are needed to train a policy that works on the ground truth system when the differentiable engine is used for training against training the policy directly on the ground truth system.
Spring-Rod System Identification via Differentiable Physics Engine
We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies.
Task-driven Perception and Manipulation for Constrained Placement of Unknown Objects
The effectiveness of the proposed approach is demonstrated by developing a robotic system that picks a previously unseen object from a table-top and places it in a constrained space.
Robotics
Tools for Data-driven Modeling of Within-Hand Manipulation with Underactuated Adaptive Hands
Precise in-hand manipulation is an important skill for a robot to perform tasks in human environments.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines
We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies.
That and There: Judging the Intent of Pointing Actions with Robotic Arms
This work proposes a set of interpretive principles for how a robotic arm can use pointing actions to communicate task information to people by extending existing models from the related literature.
Scene-level Pose Estimation for Multiple Instances of Densely Packed Objects
To evaluate this method, a dataset of densely packed objects with challenging setups for state-of-the-art approaches is collected.
A Summary of the 4th International Workshop on Recovering 6D Object Pose
The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation.
Physics-based Scene-level Reasoning for Object Pose Estimation in Clutter
This work proposes an autonomous process for pose estimation that spans from data generation to scene-level reasoning and self-learning.
Robust 6D Object Pose Estimation with Stochastic Congruent Sets
The pointsets are then matched to congruent sets on the 3D object model to generate pose estimates.
