3 research outputs found
Generating Robust Supervision for Learning-Based Visual Navigation Using Hamilton-Jacobi Reachability
In Bansal et al. (2019), a novel visual navigation framework that combines
learning-based and model-based approaches has been proposed. Specifically, a
Convolutional Neural Network (CNN) predicts a waypoint that is used by the
dynamics model for planning and tracking a trajectory to the waypoint. However,
the CNN inevitably makes prediction errors which often lead to collisions in
cluttered and tight spaces. In this paper, we present a novel Hamilton-Jacobi
(HJ) reachability-based method to generate supervision for the CNN for waypoint
prediction in an unseen environment. By modeling CNN prediction error as
"disturbances" in robot's dynamics, our generated waypoints are robust to these
disturbances, and consequently to the prediction errors. Moreover, using
globally optimal HJ reachability analysis leads to predicting waypoints that
are time-efficient and avoid greedy behavior. Through simulations and hardware
experiments, we demonstrate the advantages of the proposed approach on
navigating through cluttered, narrow indoor environments.Comment: Learning for Dynamics and Control (L4DC) 202
LBGP: Learning Based Goal Planning for Autonomous Following in Front
This paper investigates a hybrid solution which combines deep reinforcement
learning (RL) and classical trajectory planning for the following in front
application. Here, an autonomous robot aims to stay ahead of a person as the
person freely walks around. Following in front is a challenging problem as the
user's intended trajectory is unknown and needs to be estimated, explicitly or
implicitly, by the robot. In addition, the robot needs to find a feasible way
to safely navigate ahead of human trajectory. Our deep RL module implicitly
estimates human trajectory and produces short-term navigational goals to guide
the robot. These goals are used by a trajectory planner to smoothly navigate
the robot to the short-term goals, and eventually in front of the user. We
employ curriculum learning in the deep RL module to efficiently achieve a high
return. Our system outperforms the state-of-the-art in following ahead and is
more reliable compared to end-to-end alternatives in both the simulation and
real world experiments. In contrast to a pure deep RL approach, we demonstrate
zero-shot transfer of the trained policy from simulation to the real world
Prediction-Based Reachability for Collision Avoidance in Autonomous Driving
Safety is an important topic in autonomous driving since any collision may
cause serious damage to people and the environment. Hamilton-Jacobi (HJ)
Reachability is a formal method that verifies safety in multi-agent interaction
and provides a safety controller for collision avoidance. However, due to the
worst-case assumption on the car's future actions, reachability might result in
too much conservatism such that the normal operation of the vehicle is largely
hindered. In this paper, we leverage the power of trajectory prediction, and
propose a prediction-based reachability framework for the safety controller.
Instead of always assuming for the worst-case, we first cluster the car's
behaviors into multiple driving modes, e.g. left turn or right turn. Under each
mode, a reachability-based safety controller is designed based on a less
conservative action set. For online purpose, we first utilize the trajectory
prediction and our proposed mode classifier to predict the possible modes, and
then deploy the corresponding safety controller. Through simulations in a
T-intersection and an 8-way roundabout, we demonstrate that our
prediction-based reachability method largely avoids collision between two
interacting cars and reduces the conservatism that the safety controller brings
to the car's original operations