290 research outputs found
Behavior Mixing with Minimum Global and Subgroup Connectivity Maintenance for Large-Scale Multi-Robot Systems
In many cases the multi-robot systems are desired to execute simultaneously
multiple behaviors with different controllers, and sequences of behaviors in
real time, which we call \textit{behavior mixing}. Behavior mixing is
accomplished when different subgroups of the overall robot team change their
controllers to collectively achieve given tasks while maintaining connectivity
within and across subgroups in one connected communication graph. In this
paper, we present a provably minimum connectivity maintenance framework to
ensure the subgroups and overall robot team stay connected at all times while
providing the highest freedom for behavior mixing. In particular, we propose a
real-time distributed Minimum Connectivity Constraint Spanning Tree (MCCST)
algorithm to select the minimum inter-robot connectivity constraints preserving
subgroup and global connectivity that are \textit{least likely to be violated}
by the original controllers. With the employed safety and connectivity barrier
certificates for the activated connectivity constraints and collision
avoidance, the behavior mixing controllers are thus minimally modified from the
original controllers. We demonstrate the effectiveness and scalability of our
approach via simulations of up to 100 robots with multiple behaviors.Comment: To appear in Proceedings of IEEE International Conference on Robotics
and Automation (ICRA) 202
Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation
Decentralized control schemes are increasingly favored in various domains
that involve multi-agent systems due to the need for computational efficiency
as well as general applicability to large-scale systems. However, in the
absence of an explicit global coordinator, it is hard for distributed agents to
determine how to efficiently interact with others. In this paper, we present a
risk-aware decentralized control framework that provides guidance on how much
relative responsibility share (a percentage) an individual agent should take to
avoid collisions with others while moving efficiently without direct
communications. We propose a novel Control Barrier Function (CBF)-inspired risk
measurement to characterize the aggregate risk agents face from potential
collisions under motion uncertainty. We use this measurement to allocate
responsibility shares among agents dynamically and develop risk-aware
decentralized safe controllers. In this way, we are able to leverage the
flexibility of robots with lower risk to improve the motion flexibility for
those with higher risk, thus achieving improved collective safety. We
demonstrate the validity and efficiency of our proposed approach through two
examples: ramp merging in autonomous driving and a multi-agent
position-swapping game
Text Assisted Insight Ranking Using Context-Aware Memory Network
Extracting valuable facts or informative summaries from multi-dimensional
tables, i.e. insight mining, is an important task in data analysis and business
intelligence. However, ranking the importance of insights remains a challenging
and unexplored task. The main challenge is that explicitly scoring an insight
or giving it a rank requires a thorough understanding of the tables and costs a
lot of manual efforts, which leads to the lack of available training data for
the insight ranking problem. In this paper, we propose an insight ranking model
that consists of two parts: A neural ranking model explores the data
characteristics, such as the header semantics and the data statistical
features, and a memory network model introduces table structure and context
information into the ranking process. We also build a dataset with text
assistance. Experimental results show that our approach largely improves the
ranking precision as reported in multi evaluation metrics.Comment: Accepted to AAAI 201
Hiding Leader's Identity in Leader-Follower Navigation through Multi-Agent Reinforcement Learning
Leader-follower navigation is a popular class of multi-robot algorithms where
a leader robot leads the follower robots in a team. The leader has specialized
capabilities or mission critical information (e.g. goal location) that the
followers lack which makes the leader crucial for the mission's success.
However, this also makes the leader a vulnerability - an external adversary who
wishes to sabotage the robot team's mission can simply harm the leader and the
whole robot team's mission would be compromised. Since robot motion generated
by traditional leader-follower navigation algorithms can reveal the identity of
the leader, we propose a defense mechanism of hiding the leader's identity by
ensuring the leader moves in a way that behaviorally camouflages it with the
followers, making it difficult for an adversary to identify the leader. To
achieve this, we combine Multi-Agent Reinforcement Learning, Graph Neural
Networks and adversarial training. Our approach enables the multi-robot team to
optimize the primary task performance with leader motion similar to follower
motion, behaviorally camouflaging it with the followers. Our algorithm
outperforms existing work that tries to hide the leader's identity in a
multi-robot team by tuning traditional leader-follower control parameters with
Classical Genetic Algorithms. We also evaluated human performance in inferring
the leader's identity and found that humans had lower accuracy when the robot
team used our proposed navigation algorithm
- …