相关论文: Tabletop Object Rearrangement: Team ACRV's Entry t…
This paper investigates manipulation of multiple unknown objects in a crowded environment. Because of incomplete knowledge due to unknown objects and occlusions in visual observations, object observations are imperfect and action success is…
Robots can provide assistance to a human by moving objects to locations around the person's body. With a well chosen initial configuration, a robot can better reach locations important to an assistive task despite model error, pose…
Geometric organization of objects into semantically meaningful arrangements pervades the built world. As such, assistive robots operating in warehouses, offices, and homes would greatly benefit from the ability to recognize and rearrange…
Reliable localization is critical for robot navigation, yet most existing systems implicitly assume that all viewing directions at a location are equally informative. In practice, localization becomes unreliable when the robot observes…
As humans, robots, and software agents increasingly share safety-critical environments, coordination must move from static task allocation to managing uncertain commitments. Existing frameworks fall short: they either assume rigid, static…
We present a framework for solving long-horizon planning problems involving manipulation of rigid objects that operates directly from a point-cloud observation, i.e. without prior object models. Our method plans in the space of object…
The recognition of actions performed by humans and the anticipation of their intentions are important enablers to yield sociable and successful collaboration in human-robot teams. Meanwhile, robots should have the capacity to deal with…
Object-centric view planning is a core component of active geometric 3D reconstruction in robotics, yet existing evaluations often conflate object complexity, planning difficulty, budget assumptions, and physical reachability constraints.…
This paper presents a novel framework for cooperative robotics competitions (coopetitions) that promote the transferability and composability of robotics modules, including software, hardware, and data, across heterogeneous robotic systems.…
We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i.e. a deep neural network. This representation allows for integrating algorithmic planning…
We study the problem of organizing a collection of objects - images, videos - into clusters, using crowdsourcing. This problem is notoriously hard for computers to do automatically, and even with crowd workers, is challenging to…
Object rearrangement has recently emerged as a key competency in robot manipulation, with practical solutions generally involving object detection, recognition, grasping and high-level planning. Goal-images describing a desired scene…
Accessibility is one of the most important features in the design of robots and their interfaces. This thesis proposes methods that improve the accessibility of robots for three different target audiences: consumers, researchers, and…
This paper proposes RUR53: an Unmanned Ground Vehicle able to autonomously navigate through, identify, and reach areas of interest; and there recognize, localize, and manipulate work tools to perform complex manipulation tasks. The proposed…
This report describes our winning submission to the Real Robot Challenge (https://real-robot-challenge.com/). The Real Robot Challenge is a three-phase dexterous manipulation competition that involves manipulating various rectangular…
The intricate and multi-stage task in dynamic public spaces like luggage trolley collection in airports presents both a promising opportunity and an ongoing challenge for automated service robots. Previous research has primarily focused on…
Deploying autonomous robots in crowded indoor environments usually requires them to have accurate dynamic obstacle perception. Although plenty of previous works in the autonomous driving field have investigated the 3D object detection…
Effectively performing object rearrangement is an essential skill for mobile manipulators, e.g., setting up a dinner table or organizing a desk. A key challenge in such problems is deciding an appropriate manipulation order for objects to…
Our team, TRAIL, consists of AI/ML laboratory members from The University of Tokyo. We leverage our extensive research experience in state-of-the-art machine learning to build general-purpose in-home service robots. We previously…
The Associating Objects with Transformers (AOT) framework has exhibited exceptional performance in a wide range of complex scenarios for video object tracking and segmentation. In this study, we convert the bounding boxes to masks in…