84 research outputs found
Stackelberg Meta-Learning for Strategic Guidance in Multi-Robot Trajectory Planning
Guided cooperation is a common task in many multi-agent teaming applications.
The planning of the cooperation is difficult when the leader robot has
incomplete information about the follower, and there is a need to learn,
customize, and adapt the cooperation plan online. To this end, we develop a
learning-based Stackelberg game-theoretic framework to address this challenge
to achieve optimal trajectory planning for heterogeneous robots. We first
formulate the guided trajectory planning problem as a dynamic Stackelberg game
and design the cooperation plans using open-loop Stackelberg equilibria. We
leverage meta-learning to deal with the unknown follower in the game and
propose a Stackelberg meta-learning framework to create online adaptive
trajectory guidance plans, where the leader robot learns a meta-best-response
model from a prescribed set of followers offline and then fast adapts to a
specific online trajectory guidance task using limited learning data. We use
simulations in three different scenarios to elaborate on the effectiveness of
our framework. Comparison with other learning approaches and no guidance cases
show that our framework provides a more time- and data-efficient planning
method in trajectory guidance tasks
Decentralized Multi-Robot Social Navigation in Constrained Environments via Game-Theoretic Control Barrier Functions
We present an approach to ensure safe and deadlock-free navigation for
decentralized multi-robot systems operating in constrained environments,
including doorways and intersections. Although many solutions have been
proposed to ensure safety, preventing deadlocks in a decentralized fashion with
global consensus remains an open problem. We first formalize the objective as a
non-cooperative, non-communicative, partially observable multi-robot navigation
problem in constrained spaces with multiple conflicting agents, which we term
as social mini-games. Our approach to ensuring safety and liveness rests on two
novel insights: (i) deadlock resolution is equivalent to deriving a mixed-Nash
equilibrium solution to a social mini-game and (ii) this mixed-Nash strategy
can be interpreted as an analogue to control barrier functions (CBFs), that can
then be integrated with standard CBFs, inheriting their safety guarantees.
Together, the standard CBF along with the mixed-Nash CBF analogue preserves
both safety and liveness. We evaluate our proposed game-theoretic navigation
algorithm in simulation as well on physical robots using F1/10 robots, a
Clearpath Jackal, as well as a Boston Dynamics Spot in a doorway, corridor
intersection, roundabout, and hallway scenario. We show that (i) our approach
results in safer and more efficient navigation compared to local planners based
on geometrical constraints, optimization, multi-agent reinforcement learning,
and auctions, (ii) our deadlock resolution strategy is the smoothest in terms
of smallest average change in velocity and path deviation, and most efficient
in terms of makespan (iii) our approach yields a flow rate of 2.8 - 3.3
(ms)^{-1 which is comparable to flow rate in human navigation at 4 (ms)^{-1}.Comment: arXiv admin note: text overlap with arXiv:2306.0881
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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
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Computing fast search heuristics for physics-based mobile robot motion planning
Mobile robots are increasingly being employed to assist responders in search and rescue missions. Robots have to navigate in dangerous areas such as collapsed buildings and hazardous sites, which can be inaccessible to humans. Tele-operating the robots can be stressing for the human operators, which are also overloaded with mission tasks and coordination overhead, so it is important to provide the robot with some degree of autonomy, to lighten up the task for the human operator and also to ensure robot safety.
Moving robots around requires reasoning, including interpretation of the environment, spatial reasoning, planning of actions (motion), and execution. This is particularly challenging when the environment is unstructured, and the terrain is \textit{harsh}, i.e. not flat and cluttered with obstacles.
Approaches reducing the problem to a 2D path planning problem fall short, and many of those who reason about the problem in 3D don't do it in a complete and exhaustive manner.
The approach proposed in this thesis is to use rigid body simulation to obtain a more truthful model of the reality, i.e. of the interaction between the robot and the environment. Such a simulation obeys the laws of physics, takes into account the geometry of the environment, the geometry of the robot, and any dynamic constraints that may be in place.
The physics-based motion planning approach by itself is also highly intractable due to the computational load required to perform state propagation combined with the exponential blowup of planning; additionally, there are more technical limitations that disallow us to use things such as state sampling or state steering, which are known to be effective in solving the problem in simpler domains.
The proposed solution to this problem is to compute heuristics that can bias the search towards the goal, so as to quickly converge towards the solution.
With such a model, the search space is a rich space, which can only contain states which are physically reachable by the robot, and also tells us enough information about the safety of the robot itself.
The overall result is that by using this framework the robot engineer has a simpler job of encoding the \textit{domain knowledge} which now consists only of providing the robot geometric model plus any constraints
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