Search Results for author:
Found 31 papers, 11 papers with code
simPLE: a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects
We propose simPLE (simulation to Pick Localize and PLacE) as a solution to precise pick-and-place.
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.
Visuotactile Affordances for Cloth Manipulation with Local Control
We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges.
SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields
This formalism is implemented in three steps: assigning a consistent local coordinate frame to the task-relevant object parts, determining the location and orientation of this coordinate frame on unseen object instances, and executing an action that brings these frames into the desired alignment.
Tac2Pose: Tactile Object Pose Estimation from the First Touch
This results in a perception model that localizes objects from the first real tactile observation.
NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields
In particular, we demonstrate that a NeRF representation of a scene can be used to train dense object descriptors.
Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation
Our performance generalizes across both object instances and 6-DoF object poses, and significantly outperforms a recent baseline that relies on 2D descriptors.
A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation
Specifying tasks with videos is a powerful technique towards acquiring novel and general robot skills.
Planning for Multi-stage Forceful Manipulation
The robot must choose a sequence of discrete actions, or strategy, such as whether to pick up an object, and the continuous parameters of each of those actions, such as how to grasp the object.
Robotics
Robotic Grasping of Fully-Occluded Objects using RF Perception
We present the design, implementation, and evaluation of RF-Grasp, a robotic system that can grasp fully-occluded objects in unknown and unstructured environments.
INeRF: Inverting Neural Radiance Fields for Pose Estimation
We then show that for complex real-world scenes from the LLFF dataset, iNeRF can improve NeRF by estimating the camera poses of novel images and using these images as additional training data for NeRF.
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering
In this paper, we present an approach to tactile pose estimation from the first touch for known objects.
A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects
We present a framework for solving long-horizon planning problems involving manipulation of rigid objects that operates directly from a point-cloud observation, i. e. without prior object models.
Tactile Dexterity: Manipulation Primitives with Tactile Feedback
This paper develops closed-loop tactile controllers for dexterous manipulation with dual-arm robotic palms.
Robotics Systems and Control Systems and Control
Accurate Vision-based Manipulation through Contact Reasoning
Planning contact interactions is one of the core challenges of many robotic tasks.
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video
Such models, however, are approximate, which limits their applicability.
Tactile Mapping and Localization from High-Resolution Tactile Imprints
This work studies the problem of shape reconstruction and object localization using a vision-based tactile sensor, GelSlim.
Graph Element Networks: adaptive, structured computation and memory
We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure.
Combining Physical Simulators and Object-Based Networks for Control
Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically.
TossingBot: Learning to Throw Arbitrary Objects with Residual Physics
In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error.
Modular meta-learning in abstract graph networks for combinatorial generalization
Modular meta-learning is a new framework that generalizes to unseen datasets by combining a small set of neural modules in different ways.
Maintaining Grasps within Slipping Bound by Monitoring Incipient Slip
The output is a dense slip field which we use to detect when small areas of the contact patch start to slip (incipient slip).
Robotics
A Convex-Combinatorial Model for Planar Caging
Caging is a promising tool which allows a robot to manipulate an object without directly reasoning about the contact dynamics involved.
Robotics
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing
An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control.
A Data-Efficient Approach to Precise and Controlled Pushing
Decades of research in control theory have shown that simple controllers, when provided with timely feedback, can control complex systems.
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
Skilled robotic manipulation benefits from complex synergies between non-prehensile (e. g. pushing) and prehensile (e. g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free.
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
Since product images are readily available for a wide range of objects (e. g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data.
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs
On the other hand, it achieves effective sampling and accurate probabilistic propagation by relying on the GP form of the system, and the sum-of-Gaussian form of the belief.
A probabilistic data-driven model for planar pushing
This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability.
Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge
The approach was part of the MIT-Princeton Team system that took 3rd- and 4th- place in the stowing and picking tasks, respectively at APC 2016.
Analysis and Observations from the First Amazon Picking Challenge
This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams.
Robotics
