5,057 research outputs found
Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot
Mobile manipulation tasks are one of the key challenges in the field of
search and rescue (SAR) robotics requiring robots with flexible locomotion and
manipulation abilities. Since the tasks are mostly unknown in advance, the
robot has to adapt to a wide variety of terrains and workspaces during a
mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and
an anthropomorphic upper body to carry out complex tasks in environments too
dangerous for humans. Due to its high number of degrees of freedom, controlling
the robot with direct teleoperation approaches is challenging and exhausting.
Supervised autonomy approaches are promising to increase quality and speed of
control while keeping the flexibility to solve unknown tasks. We developed a
set of operator assistance functionalities with different levels of autonomy to
control the robot for challenging locomotion and manipulation tasks. The
integrated system was evaluated in disaster response scenarios and showed
promising performance.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), Madrid, Spain, October 201
-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations
The paper considers a class of multi-agent Markov decision processes (MDPs),
in which the network agents respond differently (as manifested by the
instantaneous one-stage random costs) to a global controlled state and the
control actions of a remote controller. The paper investigates a distributed
reinforcement learning setup with no prior information on the global state
transition and local agent cost statistics. Specifically, with the agents'
objective consisting of minimizing a network-averaged infinite horizon
discounted cost, the paper proposes a distributed version of -learning,
-learning, in which the network agents collaborate by means of
local processing and mutual information exchange over a sparse (possibly
stochastic) communication network to achieve the network goal. Under the
assumption that each agent is only aware of its local online cost data and the
inter-agent communication network is \emph{weakly} connected, the proposed
distributed scheme is almost surely (a.s.) shown to yield asymptotically the
desired value function and the optimal stationary control policy at each
network agent. The analytical techniques developed in the paper to address the
mixed time-scale stochastic dynamics of the \emph{consensus + innovations}
form, which arise as a result of the proposed interactive distributed scheme,
are of independent interest.Comment: Submitted to the IEEE Transactions on Signal Processing, 33 page
Distributed Algorithms for Stochastic Source Seeking With Mobile Robot Networks
Autonomous robot networks are an effective tool for monitoring large-scale environmental fields. This paper proposes distributed control strategies for localizing the source of a noisy signal, which could represent a physical quantity of interest such as magnetic force, heat, radio signal, or chemical concentration. We develop algorithms specific to two scenarios: one in which the sensors have a precise model of the signal formation process and one in which a signal model is not available. In the model-free scenario, a team of sensors is used to follow a stochastic gradient of the signal field. Our approach is distributed, robust to deformations in the group geometry, does not necessitate global localization, and is guaranteed to lead the sensors to a neighborhood of a local maximum of the field. In the model-based scenario, the sensors follow a stochastic gradient of the mutual information (MI) between their expected measurements and the expected source location in a distributed manner. The performance is demonstrated in simulation using a robot sensor network to localize the source of a wireless radio signal
Distributed Estimation and Control of Algebraic Connectivity over Random Graphs
In this paper we propose a distributed algorithm for the estimation and
control of the connectivity of ad-hoc networks in the presence of a random
topology. First, given a generic random graph, we introduce a novel stochastic
power iteration method that allows each node to estimate and track the
algebraic connectivity of the underlying expected graph. Using results from
stochastic approximation theory, we prove that the proposed method converges
almost surely (a.s.) to the desired value of connectivity even in the presence
of imperfect communication scenarios. The estimation strategy is then used as a
basic tool to adapt the power transmitted by each node of a wireless network,
in order to maximize the network connectivity in the presence of realistic
Medium Access Control (MAC) protocols or simply to drive the connectivity
toward a desired target value. Numerical results corroborate our theoretical
findings, thus illustrating the main features of the algorithm and its
robustness to fluctuations of the network graph due to the presence of random
link failures.Comment: To appear in IEEE Transactions on Signal Processin
On Partially Controlled Multi-Agent Systems
Motivated by the control theoretic distinction between controllable and
uncontrollable events, we distinguish between two types of agents within a
multi-agent system: controllable agents, which are directly controlled by the
system's designer, and uncontrollable agents, which are not under the
designer's direct control. We refer to such systems as partially controlled
multi-agent systems, and we investigate how one might influence the behavior of
the uncontrolled agents through appropriate design of the controlled agents. In
particular, we wish to understand which problems are naturally described in
these terms, what methods can be applied to influence the uncontrollable
agents, the effectiveness of such methods, and whether similar methods work
across different domains. Using a game-theoretic framework, this paper studies
the design of partially controlled multi-agent systems in two contexts: in one
context, the uncontrollable agents are expected utility maximizers, while in
the other they are reinforcement learners. We suggest different techniques for
controlling agents' behavior in each domain, assess their success, and examine
their relationship.Comment: See http://www.jair.org/ for any accompanying file
Consensus problems in networks of agents with switching topology and time-delays
In this paper, we discuss consensus problems for networks of dynamic agents with fixed and switching topologies. We analyze three cases: 1) directed networks with fixed topology; 2) directed networks with switching topology; and 3) undirected networks with communication time-delays and fixed topology. We introduce two consensus protocols for networks with and without time-delays and provide a convergence analysis in all three cases. We establish a direct connection between the algebraic connectivity (or Fiedler eigenvalue) of the network and the performance (or negotiation speed) of a linear consensus protocol. This required the generalization of the notion of algebraic connectivity of undirected graphs to digraphs. It turns out that balanced digraphs play a key role in addressing average-consensus problems. We introduce disagreement functions for convergence analysis of consensus protocols. A disagreement function is a Lyapunov function for the disagreement network dynamics. We proposed a simple disagreement function that is a common Lyapunov function for the disagreement dynamics of a directed network with switching topology. A distinctive feature of this work is to address consensus problems for networks with directed information flow. We provide analytical tools that rely on algebraic graph theory, matrix theory, and control theory. Simulations are provided that demonstrate the effectiveness of our theoretical results
Interactive inference: a multi-agent model of cooperative joint actions
We advance a novel computational model of multi-agent, cooperative joint
actions that is grounded in the cognitive framework of active inference. The
model assumes that to solve a joint task, such as pressing together a red or
blue button, two (or more) agents engage in a process of interactive inference.
Each agent maintains probabilistic beliefs about the goal of the joint task
(e.g., should we press the red or blue button?) and updates them by observing
the other agent's movements, while in turn selecting movements that make his
own intentions legible and easy to infer by the other agent (i.e., sensorimotor
communication). Over time, the interactive inference aligns both the beliefs
and the behavioral strategies of the agents, hence ensuring the success of the
joint action. We exemplify the functioning of the model in two simulations. The
first simulation illustrates a ''leaderless'' joint action. It shows that when
two agents lack a strong preference about their joint task goal, they jointly
infer it by observing each other's movements. In turn, this helps the
interactive alignment of their beliefs and behavioral strategies. The second
simulation illustrates a "leader-follower" joint action. It shows that when one
agent ("leader") knows the true joint goal, it uses sensorimotor communication
to help the other agent ("follower") infer it, even if doing this requires
selecting a more costly individual plan. These simulations illustrate that
interactive inference supports successful multi-agent joint actions and
reproduces key cognitive and behavioral dynamics of "leaderless" and
"leader-follower" joint actions observed in human-human experiments. In sum,
interactive inference provides a cognitively inspired, formal framework to
realize cooperative joint actions and consensus in multi-agent systems.Comment: 32 pages, 16 figure
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