Motion planning for autonomous driving: The state of the art and future perspectives
Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …
convenience, safety advantages, and potential commercial value. Despite predictions of …
A review of the applications of multi-agent reinforcement learning in smart factories
F Bahrpeyma, D Reichelt - Frontiers in Robotics and AI, 2022 - frontiersin.org
The smart factory is at the heart of Industry 4.0 and is the new paradigm for establishing
advanced manufacturing systems and realizing modern manufacturing objectives such as …
advanced manufacturing systems and realizing modern manufacturing objectives such as …
Habitat 3.0: A co-habitat for humans, avatars and robots
We present Habitat 3.0: a simulation platform for studying collaborative human-robot tasks in
home environments. Habitat 3.0 offers contributions across three dimensions:(1) Accurate …
home environments. Habitat 3.0 offers contributions across three dimensions:(1) Accurate …
Human-in-the-loop task and motion planning for imitation learning
Imitation learning from human demonstrations can teach robots complex manipulation skills,
but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) …
but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) …
ArtVerse: a paradigm for parallel human–machine collaborative painting creation in metaverses
Currently, the development of the foundation model, metaverse, nonfungible token (NFT),
and other emerging technologies has brought profound effects on the whole art field …
and other emerging technologies has brought profound effects on the whole art field …
Learning zero-shot cooperation with humans, assuming humans are biased
There is a recent trend of applying multi-agent reinforcement learning (MARL) to train an
agent that can cooperate with humans in a zero-shot fashion without using any human data …
agent that can cooperate with humans in a zero-shot fashion without using any human data …
Adaptive coordination in social embodied rearrangement
We present the task of" Social Rearrangement", consisting of cooperative everyday tasks
like setting up the dinner table, tidying a house or unpacking groceries in a simulated multi …
like setting up the dinner table, tidying a house or unpacking groceries in a simulated multi …
[HTML][HTML] Learning and generalization of task-parameterized skills through few human demonstrations
In the field of robotics, the demand for adaptable skills capable of effectively handling
diverse situations has surpassed the reliance on repetitive tasks. To enhance the …
diverse situations has surpassed the reliance on repetitive tasks. To enhance the …
Diffusion co-policy for synergistic human-robot collaborative tasks
Modeling multimodal human behavior has been a key barrier to increasing the level of
interaction between human and robot, particularly for collaborative tasks. Our key insight is …
interaction between human and robot, particularly for collaborative tasks. Our key insight is …
Heterogeneous multi-agent zero-shot coordination by coevolution
Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a
new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some …
new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some …