Related papers: TacGNN:Learning Tactile-based In-hand Manipulation…
In this paper, we propose a method for training control policies for human-robot interactions such as handshakes or hand claps via Deep Reinforcement Learning. The policy controls a humanoid Shadow Dexterous Hand, attached to a robot arm.…
The missing signal caused by the objects being occluded or an unstable sensor is a common challenge during data collection. Such missing signals will adversely affect the results obtained from the data, and this issue is observed more…
For contact-intensive tasks, the ability to generate policies that produce comprehensive tactile-aware motions is essential. However, existing data collection and skill learning systems for dexterous manipulation often suffer from…
Having the ability to estimate an object's properties through interaction will enable robots to manipulate novel objects. Object's dynamics, specifically the friction and inertial parameters have only been estimated in a lab environment…
This work presents a novel tactile perception-based method, named T-NT, for performing the needle-threading task, an application of deformable linear object (DLO) manipulation. This task is divided into two main stages: Tail-end Finding and…
Contact-rich manipulation is difficult for robots to execute and requires accurate perception of the environment. In some scenarios, vision is occluded. The robot can then no longer obtain real-time scene state information through visual…
Data-driven approaches to tactile sensing aim to overcome the complexity of accurately modeling contact with soft materials. However, their widespread adoption is impaired by concerns about data efficiency and the capability to generalize…
In robots, nonprehensile manipulation operations such as pushing are a useful way of moving large, heavy or unwieldy objects, moving multiple objects at once, or reducing uncertainty in the location or pose of objects. In this study, we…
Tactile sensors supply useful information during the interaction with an object that can be used for assessing the stability of a grasp. Most of the previous works on this topic processed tactile readings as signals by calculating…
One of the most efficient ways for a learning-based robotic arm to learn to process complex tasks as human, is to directly learn from observing how human complete those tasks, and then imitate. Our idea is based on success of Deep…
We investigate how high-resolution tactile sensors can be utilized in combination with vision and depth sensing, to improve grasp stability prediction. Recent advances in simulating high-resolution tactile sensing, in particular the TACTO…
Recent advances have been made in learning of grasps for fully actuated hands. A typical approach learns the target locations of finger links on the object. When a new object must be grasped, new finger locations are generated, and a…
Reusing the tactile knowledge of some previously-explored objects helps us humans to easily recognize the tactual properties of new objects. In this master thesis, we enable arobotic arm equipped with multi-modal artificial skin, like…
Observing a human demonstrator manipulate objects provides a rich, scalable and inexpensive source of data for learning robotic policies. However, transferring skills from human videos to a robotic manipulator poses several challenges, not…
We study the problem of object retrieval in scenarios where visual sensing is absent, object shapes are unknown beforehand and objects can move freely, like grabbing objects out of a drawer. Successful solutions require localizing free…
The accurate detection and grasping of transparent objects are challenging but of significance to robots. Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant light conditions is…
In-hand manipulation tasks, particularly in human-inspired robotic systems, must rely on distributed tactile sensing to achieve precise control across a wide variety of tasks. However, the optimal configuration of this network of sensors is…
In this work, we introduce the EyeSight Hand, a novel 7 degrees of freedom (DoF) humanoid hand featuring integrated vision-based tactile sensors tailored for enhanced whole-hand manipulation. Additionally, we introduce an actuation scheme…
We explore learning-based approaches for feedback control of a dexterous five-finger hand performing non-prehensile manipulation. First, we learn local controllers that are able to perform the task starting at a predefined initial state.…
During a robot to human object handover task, several intended or unintended events may occur with the object - it may be pulled, pushed, bumped or simply held - by the human receiver. We show that it is possible to differentiate between…