Active Uncertainty Reduction for Safe and Efficient Interaction
Planning: A Shielding-Aware Dual Control Approach
- URL: http://arxiv.org/abs/2302.00171v2
- Date: Wed, 1 Nov 2023 17:33:40 GMT
- Title: Active Uncertainty Reduction for Safe and Efficient Interaction
Planning: A Shielding-Aware Dual Control Approach
- Authors: Haimin Hu, David Isele, Sangjae Bae, Jaime F. Fisac
- Abstract summary: We present a novel algorithmic approach to enable active uncertainty reduction for interactive motion planning based on the implicit dual control paradigm.
Our approach relies on sampling-based approximation of dynamic programming, leading to a model predictive control problem that can be readily solved by real-time gradient-based optimization methods.
- Score: 9.07774184840379
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The ability to accurately predict others' behavior is central to the safety
and efficiency of interactive robotics. Unfortunately, robots often lack access
to key information on which these predictions may hinge, such as other agents'
goals, attention, and willingness to cooperate. Dual control theory addresses
this challenge by treating unknown parameters of a predictive model as
stochastic hidden states and inferring their values at runtime using
information gathered during system operation. While able to optimally and
automatically trade off exploration and exploitation, dual control is
computationally intractable for general interactive motion planning. In this
paper, we present a novel algorithmic approach to enable active uncertainty
reduction for interactive motion planning based on the implicit dual control
paradigm. Our approach relies on sampling-based approximation of stochastic
dynamic programming, leading to a model predictive control problem that can be
readily solved by real-time gradient-based optimization methods. The resulting
policy is shown to preserve the dual control effect for a broad class of
predictive models with both continuous and categorical uncertainty. To ensure
the safe operation of the interacting agents, we use a runtime safety filter
(also referred to as a "shielding" scheme), which overrides the robot's dual
control policy with a safety fallback strategy when a safety-critical event is
imminent. We then augment the dual control framework with an improved variant
of the recently proposed shielding-aware robust planning scheme, which
proactively balances the nominal planning performance with the risk of
high-cost emergency maneuvers triggered by low-probability agent behaviors. We
demonstrate the efficacy of our approach with both simulated driving studies
and hardware experiments using 1/10 scale autonomous vehicles.
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