156 research outputs found
Joint Sensing and Communication Optimization in Target-Mounted STARS-Assisted Vehicular Networks: A MADRL Approach
The utilization of integrated sensing and communication (ISAC) technology has
the potential to enhance the communication performance of road side units
(RSUs) through the active sensing of target vehicles. Furthermore, installing a
simultaneous transmitting and reflecting surface (STARS) on the target vehicle
can provide an extra boost to the reflection of the echo signal, thereby
improving the communication quality for in-vehicle users. However, the design
of this target-mounted STARS system exhibits significant challenges, such as
limited information sharing and distributed STARS control. In this paper, we
propose an end-to-end multi-agent deep reinforcement learning (MADRL) framework
to tackle the challenges of joint sensing and communication optimization in the
considered target-mounted STARS assisted vehicle networks. By deploying agents
on both RSU and vehicle, the MADRL framework enables RSU and vehicle to perform
beam prediction and STARS pre-configuration using their respective local
information. To ensure efficient and stable learning for continuous
decision-making, we employ the multi-agent soft actor critic (MASAC) algorithm
and the multi-agent proximal policy optimization (MAPPO) algorithm on the
proposed MADRL framework. Extensive experimental results confirm the
effectiveness of our proposed MADRL framework in improving both sensing and
communication performance through the utilization of target-mounted STARS.
Finally, we conduct a comparative analysis and comparison of the two proposed
algorithms under various environmental conditions
Game-based Platforms for Artificial Intelligence Research
Games have been the perfect test-beds for artificial intelligence research
for the characteristics that widely exist in real-world scenarios. Learning and
optimisation, decision making in dynamic and uncertain environments, game
theory, planning and scheduling, design and education are common research areas
shared between games and real-world problems. Numerous open-sourced games or
game-based environments have been implemented for studying artificial
intelligence. In addition to single- or multi-player, collaborative or
adversarial games, there has also been growing interest in implementing
platforms for creative design in recent years. Those platforms provide ideal
benchmarks for exploring and comparing artificial intelligence ideas and
techniques. This paper reviews the game-based platforms for artificial
intelligence research, discusses the research trend induced by the evolution of
those platforms, and gives an outlook
Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing
While holding and manipulating an object, humans track the object states
through vision and touch so as to achieve complex tasks. However, nowadays the
majority of robot research perceives object states just from visual signals,
hugely limiting the robotic manipulation abilities. This work presents a
tactile-enhanced generalizable 6D pose tracking design named TEG-Track to track
previously unseen in-hand objects. TEG-Track extracts tactile kinematic cues of
an in-hand object from consecutive tactile sensing signals. Such cues are
incorporated into a geometric-kinematic optimization scheme to enhance existing
generalizable visual trackers. To test our method in real scenarios and enable
future studies on generalizable visual-tactile tracking, we collect a real
visual-tactile in-hand object pose tracking dataset. Experiments show that
TEG-Track significantly improves state-of-the-art generalizable 6D pose
trackers in both synthetic and real cases
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