22 research outputs found
Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans
We propose a method to tackle the problem of mapless collision-avoidance
navigation where humans are present using 2D laser scans. Our proposed method
uses ego-safety to measure collision from the robot's perspective while
social-safety to measure the impact of our robot's actions on surrounding
pedestrians. Specifically, the social-safety part predicts the intrusion impact
of our robot's action into the interaction area with surrounding humans. We
train the policy using reinforcement learning on a simple simulator and
directly evaluate the learned policy in Gazebo and real robot tests.
Experiments show the learned policy can be smoothly transferred without any
fine tuning. We observe that our method demonstrates time-efficient path
planning behavior with high success rate in mapless navigation tasks.
Furthermore, we test our method in a navigation among dynamic crowds task
considering both low and high volume traffic. Our learned policy demonstrates
cooperative behavior that actively drives our robot into traffic flows while
showing respect to nearby pedestrians. Evaluation videos are at
https://sites.google.com/view/ssw-batmanComment: Accepted in ICRA 202
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning
For robotic vehicles to navigate robustly and safely in unseen environments,
it is crucial to decide the most suitable navigation policy. However, most
existing deep reinforcement learning based navigation policies are trained with
a hand-engineered curriculum and reward function which are difficult to be
deployed in a wide range of real-world scenarios. In this paper, we propose a
framework to learn a family of low-level navigation policies and a high-level
policy for deploying them. The main idea is that, instead of learning a single
navigation policy with a fixed reward function, we simultaneously learn a
family of policies that exhibit different behaviors with a wide range of reward
functions. We then train the high-level policy which adaptively deploys the
most suitable navigation skill. We evaluate our approach in simulation and the
real world and demonstrate that our method can learn diverse navigation skills
and adaptively deploy them. We also illustrate that our proposed hierarchical
learning framework presents explainability by providing semantics for the
behavior of an autonomous agent.Comment: ICRA 2023. First two authors contributed equally. Code at
https://github.com/leekwoon/hrl-na