Related papers: Contact-Implicit Model Predictive Control for Dext…
Dexterous manipulation is a crucial yet highly complex challenge in humanoid robotics, demanding precise, adaptable, and sample-efficient learning methods. As humanoid robots are usually designed to operate in human-centric environments and…
Existing grasp controllers usually either only support finger-tip grasps or need explicit configuration of the inner forces. We propose a novel grasp controller that supports arbitrary grasp types, including power grasps with…
Dexterous in-hand manipulation for a multi-fingered anthropomorphic hand is extremely difficult because of the high-dimensional state and action spaces, rich contact patterns between the fingers and objects. Even though deep reinforcement…
Dexterous manipulation through imitation learning has gained significant attention in robotics research. The collection of high-quality expert data holds paramount importance when using imitation learning. The existing approaches for…
Controlling hands in high-dimensional action space has been a longstanding challenge, yet humans naturally perform dexterous tasks with ease. In this paper, we draw inspiration from the concept of internal model exhibited in human behavior…
Dexterous manipulation with contact-rich interactions is crucial for advanced robotics. While recent diffusion-based planning approaches show promise for simple manipulation tasks, they often produce unrealistic ghost states (e.g., the…
Performing in-hand, contact-rich, and long-horizon dexterous manipulation remains an unsolved challenge in robotics. Prior hand dexterity works have considered each of these three challenges in isolation, yet do not combine these skills…
Manipulating objects to achieve desired goal states is a basic but important skill for dexterous manipulation. Human hand motions demonstrate proficient manipulation capability, providing valuable data for training robots with multi-finger…
Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills, making them an appealing component for general-purpose robotic manipulators. However, such hands pose a major challenge for autonomous control, due to…
The inherent difficulty and limited scalability of collecting manipulation data using multi-fingered robot hand hardware platforms have resulted in severe data scarcity, impeding research on data-driven dexterous manipulation policy…
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,…
In-hand manipulation of tools using dexterous hands in real-world is an underexplored problem in the literature. In addition to more complex geometry and larger size of the tools compared to more commonly used objects like cubes or…
Dexterous in-hand manipulation in robotics, particularly with multi-fingered robotic hands, poses significant challenges due to the intricate avoidance of collisions among fingers and the object being manipulated. Collision-free paths for…
A key challenge in contact-rich dexterous manipulation is the need to jointly reason over geometry, kinematic constraints, and intricate, nonsmooth contact dynamics. End-to-end visuomotor policies bypass this structure, but often require…
Modeling dexterous hand-object interactions is challenging as it requires understanding how subtle finger motions influence the environment through contact with objects. While recent world models address interaction modeling, they typically…
This paper uses a mobile manipulator with a collaborative robotic arm to manipulate objects beyond the robot's maximum payload. It proposes a single-shot probabilistic roadmap-based method to plan and optimize manipulation motion with…
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.…
In-hand object manipulation is an important capability for dexterous manipulation. In this paper, we introduce a modeling and planning framework for in-hand object reconfiguration, focusing on frictional patch contacts between the robot's…
We present a general approach for controlling robotic systems that make and break contact with their environments. Contact-implicit model predictive control (CI-MPC) generalizes linear MPC to contact-rich settings by utilizing a bi-level…
Dexterous manipulation has received considerable attention in recent research. Predominantly, existing studies have concentrated on reinforcement learning methods to address the substantial degrees of freedom in hand movements. Nonetheless,…