18 research outputs found
DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments
We propose DeepIPC, an end-to-end autonomous driving model that handles both
perception and control tasks in driving a vehicle. The model consists of two
main parts, perception and controller modules. The perception module takes an
RGBD image to perform semantic segmentation and bird's eye view (BEV) semantic
mapping along with providing their encoded features. Meanwhile, the controller
module processes these features with the measurement of GNSS locations and
angular speed to estimate waypoints that come with latent features. Then, two
different agents are used to translate waypoints and latent features into a set
of navigational controls to drive the vehicle. The model is evaluated by
predicting driving records and performing automated driving under various
conditions in real environments. The experimental results show that DeepIPC
achieves the best drivability and multi-task performance even with fewer
parameters compared to the other models. Codes are available at
https://github.com/oskarnatan/DeepIPC
Structure from Action: Learning Interactions for Articulated Object 3D Structure Discovery
Articulated objects are abundant in daily life. Discovering their parts,
joints, and kinematics is crucial for robots to interact with these objects. We
introduce Structure from Action (SfA), a framework that discovers the 3D part
geometry and joint parameters of unseen articulated objects via a sequence of
inferred interactions. Our key insight is that 3D interaction and perception
should be considered in conjunction to construct 3D articulated CAD models,
especially in the case of categories not seen during training. By selecting
informative interactions, SfA discovers parts and reveals initially occluded
surfaces, like the inside of a closed drawer. By aggregating visual
observations in 3D, SfA accurately segments multiple parts, reconstructs part
geometry, and infers all joint parameters in a canonical coordinate frame. Our
experiments demonstrate that a single SfA model trained in simulation can
generalize to many unseen object categories with unknown kinematic structures
and to real-world objects. Code and data will be publicly available
Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelerated
by virtual learning environments such as video games, where novel algorithms
and ideas can be quickly tested in a safe and reproducible manner. We introduce
the Google Research Football Environment, a new reinforcement learning
environment where agents are trained to play football in an advanced,
physics-based 3D simulator. The resulting environment is challenging, easy to
use and customize, and it is available under a permissive open-source license.
In addition, it provides support for multiplayer and multi-agent experiments.
We propose three full-game scenarios of varying difficulty with the Football
Benchmarks and report baseline results for three commonly used reinforcement
algorithms (IMPALA, PPO, and Ape-X DQN). We also provide a diverse set of
simpler scenarios with the Football Academy and showcase several promising
research directions