1,928 research outputs found
Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control
Reliable autonomous navigation requires adapting the control policy of a
mobile robot in response to dynamics changes in different operational
conditions. Hand-designed dynamics models may struggle to capture model
variations due to a limited set of parameters. Data-driven dynamics learning
approaches offer higher model capacity and better generalization but require
large amounts of state-labeled data. This paper develops an approach for
learning robot dynamics directly from point-cloud observations, removing the
need and associated errors of state estimation, while embedding Hamiltonian
structure in the dynamics model to improve data efficiency. We design an
observation-space loss that relates motion prediction from the dynamics model
with motion prediction from point-cloud registration to train a Hamiltonian
neural ordinary differential equation. The learned Hamiltonian model enables
the design of an energy-shaping model-based tracking controller for rigid-body
robots. We demonstrate dynamics learning and tracking control on a real
nonholonomic wheeled robot.Comment: 8 pages, 6 figure
Secure Trajectory Planning Against Undetectable Spoofing Attacks
This paper studies, for the first time, the trajectory planning problem in
adversarial environments, where the objective is to design the trajectory of a
robot to reach a desired final state despite the unknown and arbitrary action
of an attacker. In particular, we consider a robot moving in a two-dimensional
space and equipped with two sensors, namely, a Global Navigation Satellite
System (GNSS) sensor and a Radio Signal Strength Indicator (RSSI) sensor. The
attacker can arbitrarily spoof the readings of the GNSS sensor and the robot
control input so as to maximally deviate his trajectory from the nominal
precomputed path. We derive explicit and constructive conditions for the
existence of undetectable attacks, through which the attacker deviates the
robot trajectory in a stealthy way. Conversely, we characterize the existence
of secure trajectories, which guarantee that the robot either moves along the
nominal trajectory or that the attack remains detectable. We show that secure
trajectories can only exist between a subset of states, and provide a numerical
mechanism to compute them. We illustrate our findings through several numerical
studies, and discuss that our methods are applicable to different models of
robot dynamics, including unicycles. More generally, our results show how
control design affects security in systems with nonlinear dynamics.Comment: Accepted for publication in Automatic
LEMURS: Learning Distributed Multi-Robot Interactions
This paper presents LEMURS, an algorithm for learning scalable multi-robot
control policies from cooperative task demonstrations. We propose a
port-Hamiltonian description of the multi-robot system to exploit universal
physical constraints in interconnected systems and achieve closed-loop
stability. We represent a multi-robot control policy using an architecture that
combines self-attention mechanisms and neural ordinary differential equations.
The former handles time-varying communication in the robot team, while the
latter respects the continuous-time robot dynamics. Our representation is
distributed by construction, enabling the learned control policies to be
deployed in robot teams of different sizes. We demonstrate that LEMURS can
learn interactions and cooperative behaviors from demonstrations of multi-agent
navigation and flocking tasks.Comment: In Submissio
A Real-Time Solver For Time-Optimal Control Of Omnidirectional Robots with Bounded Acceleration
We are interested in the problem of time-optimal control of omnidirectional
robots with bounded acceleration (TOC-ORBA). While there exist approximate
solutions for such robots, and exact solutions with unbounded acceleration,
exact solvers to the TOC-ORBA problem have remained elusive until now. In this
paper, we present a real-time solver for true time-optimal control of
omnidirectional robots with bounded acceleration. We first derive the general
parameterized form of the solution to the TOC-ORBA problem by application of
Pontryagin's maximum principle. We then frame the boundary value problem of
TOC-ORBA as an optimization problem over the parametrized control space. To
overcome local minima and poor initial guesses to the optimization problem, we
introduce a two-stage optimal control solver (TSOCS): The first stage computes
an upper bound to the total time for the TOC-ORBA problem and holds the time
constant while optimizing the parameters of the trajectory to approach the
boundary value conditions. The second stage uses the parameters found by the
first stage, and relaxes the constraint on the total time to solve for the
parameters of the complete TOC-ORBA problem. We further implement TSOCS as a
closed loop controller to overcome actuation errors on real robots in
real-time. We empirically demonstrate the effectiveness of TSOCS in simulation
and on real robots, showing that 1) it runs in real time, generating solutions
in less than 0.5ms on average; 2) it generates faster trajectories compared to
an approximate solver; and 3) it is able to solve TOC-ORBA problems with
non-zero final velocities that were previously unsolvable in real-time
Physics-Informed Multi-Agent Reinforcement Learning for Distributed Multi-Robot Problems
The networked nature of multi-robot systems presents challenges in the
context of multi-agent reinforcement learning. Centralized control policies do
not scale with increasing numbers of robots, whereas independent control
policies do not exploit the information provided by other robots, exhibiting
poor performance in cooperative-competitive tasks. In this work we propose a
physics-informed reinforcement learning approach able to learn distributed
multi-robot control policies that are both scalable and make use of all the
available information to each robot. Our approach has three key
characteristics. First, it imposes a port-Hamiltonian structure on the policy
representation, respecting energy conservation properties of physical robot
systems and the networked nature of robot team interactions. Second, it uses
self-attention to ensure a sparse policy representation able to handle
time-varying information at each robot from the interaction graph. Third, we
present a soft actor-critic reinforcement learning algorithm parameterized by
our self-attention port-Hamiltonian control policy, which accounts for the
correlation among robots during training while overcoming the need of value
function factorization. Extensive simulations in different multi-robot
scenarios demonstrate the success of the proposed approach, surpassing previous
multi-robot reinforcement learning solutions in scalability, while achieving
similar or superior performance (with averaged cumulative reward up to x2
greater than the state-of-the-art with robot teams x6 larger than the number of
robots at training time).Comment: This paper is under review at IEEE T-R
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