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Found 10 papers, 6 papers with code
Proprioceptive Learning with Soft Polyhedral Networks
Proprioception is the "sixth sense" that detects limb postures with motor neurons.
Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater
Robots play a critical role as the physical agent of human operators in exploring the ocean.
Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping
This paper presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping.
Robotics
DeepClaw: A Robotic Hardware Benchmarking Platform for Learning Object Manipulation
We present benchmarking results of the DeepClaw system for a baseline Tic-Tac-Toe task, a bin-clearing task, and a jigsaw puzzle task using three sets of standard robotic hardware.
Robotics
A Lobster-inspired Robotic Glove for Hand Rehabilitation
This paper presents preliminary results of the design, development, and evaluation of a hand rehabilitation glove fabricated using lobster-inspired hybrid design with rigid and soft components for actuation.
Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger
Robotic fingers made of soft material and compliant structures usually lead to superior adaptation when interacting with the unstructured physical environment.
Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance
Many researchers have identified robotics as a potential solution to the aging population faced by many developed and developing countries.
Rigid-Soft Interactive Learning for Robust Grasping
We use soft, stuffed toys for training, instead of everyday objects, to reduce the integration complexity and computational burden and exploit such rigid-soft interaction by changing the gripper fingers to the soft ones when dealing with rigid, daily-life items such as the Yale-CMU-Berkeley (YCB) objects.
Reconfigurable Design for Omni-adaptive Grasp Learning
Such reconfigurable design with these omni-adaptive fingers enables us to systematically investigate the optimal arrangement of the fingers towards robust grasping.
Logical Learning Through a Hybrid Neural Network with Auxiliary Inputs
In this paper, we describe the design of a hybrid neural network for logical learning that is similar to the human reasoning through the introduction of an auxiliary input, namely the indicators, that act as the hints to suggest logical outcomes.
