12,664 research outputs found
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
A key challenge in multi-robot and multi-agent systems is generating
solutions that are robust to other self-interested or even adversarial parties
who actively try to prevent the agents from achieving their goals. The
practicality of existing works addressing this challenge is limited to only
small-scale synchronous decision-making scenarios or a single agent planning
its best response against a single adversary with fixed, procedurally
characterized strategies. In contrast this paper considers a more realistic
class of problems where a team of asynchronous agents with limited observation
and communication capabilities need to compete against multiple strategic
adversaries with changing strategies. This problem necessitates agents that can
coordinate to detect changes in adversary strategies and plan the best response
accordingly. Our approach first optimizes a set of stratagems that represent
these best responses. These optimized stratagems are then integrated into a
unified policy that can detect and respond when the adversaries change their
strategies. The near-optimality of the proposed framework is established
theoretically as well as demonstrated empirically in simulation and hardware
Synthesis of Switching Protocols from Temporal Logic Specifications
We propose formal means for synthesizing switching protocols that determine the sequence in which the modes of a switched system are activated to satisfy certain high-level specifications in linear temporal logic. The synthesized protocols are robust against exogenous disturbances on the continuous dynamics. Two types of finite transition systems, namely under- and over-approximations, that abstract the behavior of the underlying continuous dynamics are defined. In particular, we show that the discrete synthesis problem for an under-approximation can be formulated as a model checking problem, whereas that for an over-approximation can be transformed into a two-player game. Both of these formulations are amenable to efficient, off-the-shelf software tools. By construction, existence of a discrete switching strategy for the discrete synthesis problem guarantees the existence of a continuous switching protocol for the continuous synthesis problem, which can be implemented at the continuous level to ensure the correctness of the nonlinear switched system. Moreover, the proposed framework can be straightforwardly extended to accommodate specifications that require reacting to possibly adversarial external events. Finally, these results are illustrated using three examples from different application domains
Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
We present an approach for mobile robots to learn to navigate in dynamic
environments with pedestrians via raw depth inputs, in a socially compliant
manner. To achieve this, we adopt a generative adversarial imitation learning
(GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our
approach overcomes the disadvantages of previous methods, as they heavily
depend on the full knowledge of the location and velocity information of nearby
pedestrians, which not only requires specific sensors, but also the extraction
of such state information from raw sensory input could consume much computation
time. In this paper, our proposed GAIL-based model performs directly on raw
depth inputs and plans in real-time. Experiments show that our GAIL-based
approach greatly improves the safety and efficiency of the behavior of mobile
robots from pure behavior cloning. The real-world deployment also shows that
our method is capable of guiding autonomous vehicles to navigate in a socially
compliant manner directly through raw depth inputs. In addition, we release a
simulation plugin for modeling pedestrian behaviors based on the social force
model.Comment: ICRA 2018 camera-ready version. 7 pages, video link:
https://www.youtube.com/watch?v=0hw0GD3lkA
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