相关论文: DexSkin: High-Coverage Conformable Robotic Skin fo…
Continuous in-hand manipulation is an important physical interaction skill, where tactile sensing provides indispensable contact information to enable dexterous manipulation of small objects. This work proposed a framework for end-to-end…
Contact-rich manipulation has become increasingly important in robot learning. However, previous studies on robot learning datasets have focused on rigid objects and underrepresented the diversity of pressure conditions for real-world…
In this paper, we study integrated estimation and control of soft robots. A significant challenge in deploying closed loop controllers is reliable proprioception via integrated sensing in soft robots. Despite the considerable advances…
Tactile sensing is vital for human dexterous manipulation, however, it has not been widely used in robotics. Compact, low-cost sensing platforms can facilitate a change, but unlike their popular optical counterparts, they are difficult to…
This paper presents a novel human-robot interaction (HRI) framework that enables intuitive gesture-driven control through a capacitance-based woven tactile skin. Unlike conventional interfaces that rely on panels or handheld devices, the…
Tactile sensing is an essential perception for robots to complete dexterous tasks. As a promising tactile sensing technique, vision-based tactile sensors have been developed to improve robot performance in manipulation and grasping. Here we…
Robots which interact with the physical world will benefit from a fine-grained tactile understanding of objects and surfaces. Additionally, for certain tasks, robots may need to know the haptic properties of an object before touching it. To…
Despite significant advances in touch and force transduction, tactile sensing is still far from ubiquitous in robotic manipulation. Existing methods for building touch sensors have proven difficult to integrate into robot fingers due to…
Humans seemingly incorporate potential touch signals in their perception. Our goal is to equip robots with a similar capability, which we term Imagine2touch. Imagine2touch aims to predict the expected touch signal based on a visual patch…
To enable general-purpose robots, we will require the robot to operate daily articulated objects as humans do. Current robot manipulation has heavily relied on using a parallel gripper, which restricts the robot to a limited set of objects.…
Dexterous manipulation requires careful reasoning over extrinsic contacts. The prevalence of deforming tools in human environments, the use of deformable sensors, and the increasing number of soft robots yields a need for approaches that…
Fine dexterous manipulation requires reactive control based on rich sensing of manipulator-object interactions. Tactile sensing arrays provide rich contact information across the manipulator's surface. However their implementation faces two…
We introduce an efficient approach for learning dexterous grasping with minimal data, advancing robotic manipulation capabilities across different robotic hands. Unlike traditional methods that require millions of grasp labels for each…
Dexterous hands enable concurrent prehensile and nonprehensile manipulation, such as holding one object while interacting with another, a capability essential for everyday tasks yet underexplored in robotics. Learning such long-horizon,…
Effective communication of robotic touch intent is a key factor in promoting safe and predictable physical human-robot interaction (pHRI). While intent communication has been widely studied, existing approaches lack the spatial specificity…
Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation,…
Deformable object manipulation is a classical and challenging research area in robotics. Compared with rigid object manipulation, this problem is more complex due to the deformation properties including elastic, plastic, and elastoplastic…
Development of dexterous manipulation hardware has primarily focused on hands and grippers. However, these end-effectors are often paired with bulky and highly stiff wrists that limit performance in human environments. More designs have…
Recent progress in reinforcement learning (RL) and tactile sensing has significantly advanced dexterous manipulation. However, these methods often utilize simplified tactile signals due to the gap between tactile simulation and the real…
Tactile sensing is critical to fine-grained, contact-rich manipulation tasks, such as insertion and assembly. Prior research has shown the possibility of learning tactile-guided policy from teleoperated demonstration data. However, to…