21,218 research outputs found
Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties
Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions (CBF) have emerged as a computationally efficient tool to guarantee safety in multi-agent environments, but they assume perfect knowledge of both the robot dynamics and other agents' dynamics. While knowledge of the robot's dynamics might be reasonably well known, the heterogeneity of agents in real-world environments means there will always be considerable uncertainty in our prediction of other agents' dynamics. This work aims to learn high-confidence bounds for these dynamic uncertainties using Matrix-Variate Gaussian Process models, and incorporates them into a robust multi-agent CBF framework. We transform the resulting min-max robust CBF into a quadratic program, which can be efficiently solved in real time. We verify via simulation results that the nominal multi-agent CBF is often violated during agent interactions, whereas our robust formulation maintains safety with a much higher probability and adapts to learned uncertainties
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Understanding Model-Based Reinforcement Learning and its Application in Safe Reinforcement Learning
Model-based reinforcement learning algorithms have been shown to achieve successful results on various continuous control benchmarks, but the understanding of model-based methods is limited. We try to interpret how model-based method works through novel experiments on state-of-the-art algorithms with an emphasis on the model learning part. We evaluate the role of the model learning in policy optimization and propose methods to learn a more accurate model. With a better understanding of model-based reinforcement learning, we then apply model-based methods to solve safe reinforcement learning (RL) problems with near-zero violation of hard constraints throughout training. Drawing an analogy with how humans and animals learn to perform safe actions, we break down the safe RL problem into three stages. First, we train agents in a constraint-free environment to learn a performant policy for reaching high rewards, and simultaneously learn a model of the dynamics. Second, we use model-based methods to plan safe actions and train a safeguarding policy from these actions through imitation. Finally, we propose a factored framework to train an overall policy that mixes the performant policy and the safeguarding policy. This three-step curriculum ensures near-zero violation of safety constraints at all times. As an advantage of model-based method, the sample complexity required at the second and third steps of the process is significantly lower than model-free methods and can enable online safe learning. We demonstrate the effectiveness of our methods in various continuous control problems and analyze the advantages over state-of-the-art approaches
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
Risk Management in the Arctic Offshore: Wicked Problems Require New Paradigms
Recent project-management literature and high-profile disasters—the financial crisis, the BP
Deepwater Horizon oil spill, and the Fukushima nuclear accident—illustrate the flaws of
traditional risk models for complex projects. This research examines how various groups with
interests in the Arctic offshore define risks. The findings link the wicked problem framework and
the emerging paradigm of Project Management of the Second Order (PM-2). Wicked problems
are problems that are unstructured, complex, irregular, interactive, adaptive, and novel. The
authors synthesize literature on the topic to offer strategies for navigating wicked problems,
provide new variables to deconstruct traditional risk models, and integrate objective and
subjective schools of risk analysis
Data-driven Safe Control of Linear Systems Under Epistemic and Aleatory Uncertainties
Safe control of constrained linear systems under both epistemic and aleatory
uncertainties is considered. The aleatory uncertainty characterizes random
noises and is modeled by a probability distribution function (PDF) and the
epistemic uncertainty characterizes the lack of knowledge on the system
dynamics. Data-based probabilistic safe controllers are designed for the cases
where the noise PDF is 1) zero-mean Gaussian with a known covariance, 2)
zero-mean Gaussian with an uncertain covariance, and 3) zero-mean non-Gaussian
with an unknown distribution. Easy-to-check model-based conditions for
guaranteeing probabilistic safety are provided for the first case by
introducing probabilistic contractive sets. These results are then extended to
the second and third cases by leveraging distributionally-robust probabilistic
safe control and conditional value-at-risk (CVaR) based probabilistic safe
control, respectively. Data-based implementations of these probabilistic safe
controllers are then considered. It is shown that data-richness requirements
for directly learning a safe controller is considerably weaker than
data-richness requirements for model-based safe control approaches that
undertake a model identification. Moreover, an upper bound on the minimal risk
level, under which the existence of a safe controller is guaranteed, is learned
using collected data. A simulation example is provided to show the
effectiveness of the proposed approach
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