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.
In this paper, we explore natural language as an expressive and flexible tool for robot correction.
Geometric organization of objects into semantically meaningful arrangements pervades the built world.
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.
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.
We use DULA in postural optimization for physical human-robot interaction tasks such as co-manipulation and teleoperation.
In this paper, we propose a novel approach to 3D deformable object manipulation leveraging a deep neural network called DeformerNet.
Ergonomics and human comfort are essential concerns in physical human-robot interaction applications.
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.
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.
Ergonomic assessment of human posture plays a vital role in understanding work-related safety and health.
Robotics Human-Computer Interaction Signal Processing
We can then formulate grasp planning as inferring the grasp configuration which maximizes the probability of grasp success.
The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems.
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.
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.
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.