12 accepted papers
Generating collision-free motion in dynamic, partially observable environments is a fundamental challenge for robotic manipulators. Classical motion planners can compute globally optimal trajectories but require full environment knowledge and are typically too slow for dynamic scenes. Neural motion…
Replicating human-like dexterity in robotic hands has been a long-standing challenge in robotics. Recently, with the rise of robot learning and humanoids, the demand for dexterous robot hands to be reliable, affordable, and easy to reproduce has grown significantly. To address these needs, we presen…
Many believe that large-scale datasets for robotics could be a key enabler of dexterous robotic policies that can generalize across diverse environments. While teleoperation provides high-fidelity datasets, its high cost limits its scalability. Instead, what if people could use their own hands, just…
Many contact-rich tasks humans perform, such as box pickup or hammering, rely on force feedback for reliable execution. However, this force information, which is readily available in most robot arms, is not commonly used in teleoperation and policy learning. Consequently, robot behavior is often lim…
To train generalist robot policies, machine learning methods often require a substantial amount of expert human teleoperation data. An ideal robot for humans collecting data is one that closely mimics them: bimanual arms and dexterous hands. However, creating such a bimanual teleoperation system wit…
Emulating human-like dexterity with robotic hands has been a long-standing challenge in robotics. In recent years, machine learning has demanded robot hands to be reliable, inexpensive and easy-to-reproduce. For the past few years we have been investigating how to address these demands. We will demo…
While there has been remarkable progress recently in the fields of manipulation and locomotion mobile manipulation remains a long-standing challenge. Compared to locomotion or static manipulation a mobile system must make a diverse range of long-horizon tasks feasible in unstructured and dynamic env…
Dexterity is often seen as a cornerstone of complex manipulation. Humans are able to perform a host of skills with their hands, from making food to operating tools. In this paper, we investigate these challenges, especially in the case of soft, deformable objects as well as complex, relatively long…
While there have been significant strides in dexterous manipulation, most of it is limited to benchmark tasks like in-hand reorientation which are of limited utility in the real world. The main benefit of dexterous hands over two-fingered ones is their ability to pickup tools and other objects (incl…
Dexterous manipulation has been a long-standing challenge in robotics. While machine learning techniques have shown some promise, results have largely been currently limited to simulation. This can be mostly attributed to the lack of suitable hardware. In this paper, we present LEAP Hand, a low-cost…
To build general robotic agents that can operate in many environments, it is often imperative for the robot to collect experience in the real world. However, this is often not feasible due to safety, time and hardware restrictions. We thus propose leveraging the next best thing as real world exper…
To realize effective heterogeneous multi-agent teams, we must be able to leverage individual agents' relative strengths. Recent work has addressed this challenge by introducing trait-based task assignment approaches that exploit the agents' relative advantages. These approaches, however, assume that…