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Found 9 papers, 0 papers with code
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery
This paper introduces a sample-efficient method that learns a robust reward function from a limited amount of ranked suboptimal demonstrations consisting of partial-view point cloud observations.
General-purpose foundation models for increased autonomy in robot-assisted surgery
The dominant paradigm for end-to-end robot learning focuses on optimizing task-specific objectives that solve a single robotic problem such as picking up an object or reaching a target position.
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.
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.
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.
A Recurrent Neural Network Approach to Roll Estimation for Needle Steering
Steerable needles are a promising technology for delivering targeted therapies in the body in a minimally-invasive fashion, as they can curve around anatomical obstacles and hone in on anatomical targets.
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.
Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning
Our results show the feasibility of a robot learning commonsense knowledge automatically from web-based textual corpora, and the power of learned commonsense reasoning models in enabling a robot to autonomously perform tasks based on incomplete natural language instructions.
