3,357 research outputs found
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
In this work, we propose a novel robot learning framework called Neural Task
Programming (NTP), which bridges the idea of few-shot learning from
demonstration and neural program induction. NTP takes as input a task
specification (e.g., video demonstration of a task) and recursively decomposes
it into finer sub-task specifications. These specifications are fed to a
hierarchical neural program, where bottom-level programs are callable
subroutines that interact with the environment. We validate our method in three
robot manipulation tasks. NTP achieves strong generalization across sequential
tasks that exhibit hierarchal and compositional structures. The experimental
results show that NTP learns to generalize well to- wards unseen tasks with
increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201
A Survey and Analysis of Multi-Robot Coordination
International audienceIn the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper
Overcoming barriers and increasing independence: service robots for elderly and disabled people
This paper discusses the potential for service robots to overcome barriers and increase independence of
elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly
people and advances in technology which will make new uses possible and provides suggestions for some of these new
applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses
the complementarity of assistive service robots and personal assistance and considers the types of applications and
users for which service robots are and are not suitable
3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs
Numerous applications require robots to operate in environments shared with
other agents such as humans or other robots. However, such shared scenes are
typically subject to different kinds of long-term semantic scene changes. The
ability to model and predict such changes is thus crucial for robot autonomy.
In this work, we formalize the task of semantic scene variability estimation
and identify three main varieties of semantic scene change: changes in the
position of an object, its semantic state, or the composition of a scene as a
whole. To represent this variability, we propose the Variable Scene Graph
(VSG), which augments existing 3D Scene Graph (SG) representations with the
variability attribute, representing the likelihood of discrete long-term change
events. We present a novel method, DeltaVSG, to estimate the variability of
VSGs in a supervised fashion. We evaluate our method on the 3RScan long-term
dataset, showing notable improvements in this novel task over existing
approaches. Our method DeltaVSG achieves a precision of 72.2% and recall of
66.8%, often mimicking human intuition about how indoor scenes change over
time. We further show the utility of VSG predictions in the task of active
robotic change detection, speeding up task completion by 62.4% compared to a
scene-change-unaware planner. We make our code available as open-source.Comment: 8 pages, 4 figures, code to be released at
https://github.com/ethz-asl/3d_vs
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