Search Results for author:
Found 27 papers, 4 papers with code
Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models
Robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments.
DefGoalNet: Contextual Goal Learning from Demonstrations For Deformable Object Manipulation
An issue arises, however, with the reliance on the specification of a goal shape.
DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objects
Applications in fields ranging from home care to warehouse fulfillment to surgical assistance require robots to reliably manipulate the shape of 3D deformable objects.
Planning Visual-Tactile Precision Grasps via Complementary Use of Vision and Touch
The robot then executes this plan using a tactile-feedback controller that enables the robot to adapt to online estimates of the object's surface to correct for errors in the initial plan.
StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects
StructDiffusion even improves the success rate of assembling physically-valid structures out of unseen objects by on average 16% over an existing multi-modal transformer model trained on specific structures.
Occlusion-Robust Multi-Sensory Posture Estimation in Physical Human-Robot Interaction
We use 2D postures from OpenPose over a single camera, and the trajectory of the interacting robot while the human performs a task.
DULA and DEBA: Differentiable Ergonomic Risk Models for Postural Assessment and Optimization in Ergonomically Intelligent pHRI
In order to retain assessment quality, while improving computational considerations, we propose a novel framework for postural assessment and optimization for ergonomically intelligent physical human-robot interaction.
Correcting Robot Plans with Natural Language Feedback
In this paper, we explore natural language as an expressive and flexible tool for robot correction.
L4KDE: Learning for KinoDynamic Tree Expansion
We study the kinodynamic variants of tree-based planning, where we have known system dynamics and kinematic constraints.
StructFormer: Learning Spatial Structure for Language-Guided Semantic Rearrangement of Novel Objects
Geometric organization of objects into semantically meaningful arrangements pervades the built world.
Learning Visual Shape Control of Novel 3D Deformable Objects from Partial-View Point Clouds
If robots could reliably manipulate the shape of 3D deformable objects, they could find applications in fields ranging from home care to warehouse fulfillment to surgical assistance.
Parallelised Diffeomorphic Sampling-based Motion Planning
We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP).
Predicting Stable Configurations for Semantic Placement of Novel Objects
We further demonstrate the ability of our planner to generate and execute diverse manipulation plans through a set of real-world experiments with a variety of objects.
Ergonomically Intelligent Physical Human-Robot Interaction: Postural Estimation, Assessment, and Optimization
We use DULA in postural optimization for physical human-robot interaction tasks such as co-manipulation and teleoperation.
DeformerNet: A Deep Learning Approach to 3D Deformable Object Manipulation
In this paper, we propose a novel approach to 3D deformable object manipulation leveraging a deep neural network called DeformerNet.
DULA: A Differentiable Ergonomics Model for Postural Optimization in Physical HRI
Ergonomics and human comfort are essential concerns in physical human-robot interaction applications.
Optimizing Hospital Room Layout to Reduce the Risk of Patient Falls
In this work, we formulate a gradient-free constrained optimization problem to generate and reconfigure the hospital room interior layout to minimize the risk of falls.
Planning under Uncertainty to Goal Distributions
We argue that goal distributions are a more appropriate goal representation than deterministic sets for many robotics applications.
Robotics
Multi-Fingered Active Grasp Learning
We show that our active grasp learning approach uses fewer training samples to produce grasp success rates comparable with the passive supervised learning method trained with grasping data generated by an analytical planner.
Robotics
In-Hand Object-Dynamics Inference using Tactile Fingertips
We show that tactile fingertips enable in-hand dynamics estimation of low mass objects.
Robotics
Is The Leader Robot an Adequate Sensor for Posture Estimation and Ergonomic Assessment of A Human Teleoperator?
Ergonomic assessment of human posture plays a vital role in understanding work-related safety and health.
Robotics Human-Computer Interaction Signal Processing
Multi-Fingered Grasp Planning via Inference in Deep Neural Networks
We can then formulate grasp planning as inferring the grasp configuration which maximizes the probability of grasp success.
Robotics
Benchmarking In-Hand Manipulation
The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems.
Robotics
Learning Continuous 3D Reconstructions for Geometrically Aware Grasping
We leverage the structure of the reconstruction network to learn a grasp success classifier which serves as the objective function for a continuous grasp optimization.
Learning to Manipulate Object Collections Using Grounded State Representations
We propose a method for sim-to-real robot learning which exploits simulator state information in a way that scales to many objects.
Active Learning of Probabilistic Movement Primitives
However, there is currently no principled guidance in the literature to determine how many demonstrations a teacher should provide and what constitutes a "good" demonstration for promoting generalization.
Robotics
Building 3D Object Models during Manipulation by Reconstruction-Aware Trajectory Optimization
We use a sampling-based trajectory generation method to explore the unseen parts of the object using the estimated conditional entropy of the GPIS model.
