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Game-Theoretic Safety Assurance for Human-Centered Robotic Systems
In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high-confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible.This dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. It first introduces a suite of novel robust optimal control formulations and algorithmic tools that permit tractable safety analysis in time-varying, multi-agent systems, as well as safe real-time robotic navigation in partially unknown environments; these approaches are demonstrated on large-scale unmanned air traffic simulation and physical quadrotor platforms. After this, it draws on Bayesian machine learning methods to translate model-based guarantees into high-confidence assurances, monitoring the reliability of predictive models in light of changing evidence about the physical system and surrounding agents. This principle is first applied to a general safety framework allowing the use of learning-based control (e.g. reinforcement learning) for safety-critical robotic systems such as drones, and then combined with insights from cognitive science and dynamic game theory to enable safe human-centered navigation and interaction; these techniques are showcased on physical quadrotors—flying in unmodeled wind and among human pedestrians—and simulated highway driving. The dissertation ends with a discussion of challenges and opportunities ahead, including the bridging of safety analysis and reinforcement learning and the need to ``close the loop'' around learning and adaptation in order to deploy increasingly advanced autonomous systems with confidence
Full-Body Torque-Level Non-linear Model Predictive Control for Aerial Manipulation
Non-linear model predictive control (nMPC) is a powerful approach to control
complex robots (such as humanoids, quadrupeds, or unmanned aerial manipulators
(UAMs)) as it brings important advantages over other existing techniques. The
full-body dynamics, along with the prediction capability of the optimal control
problem (OCP) solved at the core of the controller, allows to actuate the robot
in line with its dynamics. This fact enhances the robot capabilities and
allows, e.g., to perform intricate maneuvers at high dynamics while optimizing
the amount of energy used. Despite the many similarities between humanoids or
quadrupeds and UAMs, full-body torque-level nMPC has rarely been applied to
UAMs.
This paper provides a thorough description of how to use such techniques in
the field of aerial manipulation. We give a detailed explanation of the
different parts involved in the OCP, from the UAM dynamical model to the
residuals in the cost function. We develop and compare three different nMPC
controllers: Weighted MPC, Rail MPC, and Carrot MPC, which differ on the
structure of their OCPs and on how these are updated at every time step. To
validate the proposed framework, we present a wide variety of simulated case
studies. First, we evaluate the trajectory generation problem, i.e., optimal
control problems solved offline, involving different kinds of motions (e.g.,
aggressive maneuvers or contact locomotion) for different types of UAMs. Then,
we assess the performance of the three nMPC controllers, i.e., closed-loop
controllers solved online, through a variety of realistic simulations. For the
benefit of the community, we have made available the source code related to
this work.Comment: Submitted to Transactions on Robotics. 17 pages, 16 figure
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Design and Motion Planning for a Reconfigurable Robotic Base
A robotic platform for mobile manipulation needs to satisfy two contradicting
requirements for many real-world applications: A compact base is required to
navigate through cluttered indoor environments, while the support needs to be
large enough to prevent tumbling or tip over, especially during fast
manipulation operations with heavy payloads or forceful interaction with the
environment. This paper proposes a novel robot design that fulfills both
requirements through a versatile footprint. It can reconfigure its footprint to
a narrow configuration when navigating through tight spaces and to a wide
stance when manipulating heavy objects. Furthermore, its triangular
configuration allows for high-precision tasks on uneven ground by preventing
support switches. A model predictive control strategy is presented that unifies
planning and control for simultaneous navigation, reconfiguration, and
manipulation. It converts task-space goals into whole-body motion plans for the
new robot. The proposed design has been tested extensively with a hardware
prototype. The footprint reconfiguration allows to almost completely remove
manipulation-induced vibrations. The control strategy proves effective in both
lab experiment and during a real-world construction task.Comment: 8 pages, accepted for RA-L and IROS 202
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