7,093 research outputs found
Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning
This paper introduces a hybrid algorithm of deep reinforcement learning (RL)
and Force-based motion planning (FMP) to solve distributed motion planning
problem in dense and dynamic environments. Individually, RL and FMP algorithms
each have their own limitations. FMP is not able to produce time-optimal paths
and existing RL solutions are not able to produce collision-free paths in dense
environments. Therefore, we first tried improving the performance of recent RL
approaches by introducing a new reward function that not only eliminates the
requirement of a pre supervised learning (SL) step but also decreases the
chance of collision in crowded environments. That improved things, but there
were still a lot of failure cases. So, we developed a hybrid approach to
leverage the simpler FMP approach in stuck, simple and high-risk cases, and
continue using RL for normal cases in which FMP can't produce optimal path.
Also, we extend GA3C-CADRL algorithm to 3D environment. Simulation results show
that the proposed algorithm outperforms both deep RL and FMP algorithms and
produces up to 50% more successful scenarios than deep RL and up to 75% less
extra time to reach goal than FMP.Comment: IEEE Robotics and Automation Letters (2020
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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