Related papers: Dexterous Manipulation Transfer via Progressive Ki…
Vision-based human-to-robot handover is an important and challenging task in human-robot interaction. Recent work has attempted to train robot policies by interacting with dynamic virtual humans in simulated environments, where the policies…
This paper presents a real-time programming and parameter reconfiguration method for autonomous underwater robots in human-robot collaborative tasks. Using a set of intuitive and meaningful hand gestures, we develop a syntactically simple…
Dexterous manipulation remains a challenging robotics problem, largely due to the difficulty of collecting extensive human demonstrations for learning. In this paper, we introduce \textsc{Gen2Real}, which replaces costly human demos with…
We present DexCanvas, a large-scale hybrid real-synthetic human manipulation dataset containing 7,000 hours of dexterous hand-object interactions seeded from 70 hours of real human demonstrations, organized across 21 fundamental…
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.…
This study introduces a novel language-guided diffusion-based learning framework, DexTOG, aimed at advancing the field of task-oriented grasping (TOG) with dexterous hands. Unlike existing methods that mainly focus on 2-finger grippers,…
Dexterous robotic manipulation remains a challenging domain due to its strict demands for precision and robustness on both hardware and software. While dexterous robotic hands have demonstrated remarkable capabilities in complex tasks,…
Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology. However, dexterous grasping is much more under-explored than object grasping with parallel grippers,…
Human hand actions are quite complex, especially when they involve object manipulation, mainly due to the high dimensionality of the hand and the vast action space that entails. Imitating those actions with dexterous hand models involves…
Dexterous robotic hands are essential for performing complex manipulation tasks, yet remain difficult to train due to the challenges of demonstration collection and high-dimensional control. While reinforcement learning (RL) can alleviate…
In-hand manipulation using multiple dexterous fingers is a critical robotic skill that can reduce the reliance on large arm motions, thereby saving space and energy. This letter focuses on in-grasp object movement, which refers to…
Most object manipulation strategies for robots are based on the assumption that the object is rigid (i.e., with fixed geometry) and the goal's details have been fully specified (e.g., the exact target pose). However, there are many tasks…
Scaling real robot data is a key bottleneck in imitation learning, leading to the use of auxiliary data for policy training. While other aspects of robotic manipulation such as image or language understanding may be learned from…
Task-oriented dexterous grasping holds broad application prospects in robotic manipulation and human-object interaction. However, most existing methods still struggle to generalize across diverse objects and task instructions, as they…
A motion-based control interface promises flexible robot operations in dangerous environments by combining user intuitions with the robot's motor capabilities. However, designing a motion interface for non-humanoid robots, such as…
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…
This article investigates the challenge of achieving functional tool-use grasping with high-DoF anthropomorphic hands, with the aim of enabling anthropomorphic hands to perform tasks that require human-like manipulation and tool-use.…
This paper presents a hierarchical framework for planning and control of in-hand manipulation of a rigid object involving grasp changes using fully-actuated multifingered robotic hands. While the framework can be applied to the general…
Parameterizing finger rolling and finger-object contacts in a differentiable manner is important for formulating dexterous manipulation as a trajectory optimization problem. In contrast to previous methods which often assume simplified…
Cross-embodiment learning seeks to build generalist robots that operate across diverse morphologies, but differences in action spaces and kinematics hinder data sharing and policy transfer. This raises a central question: Is there any…