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Found 12 papers, 3 papers with code
SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
To combat this, our approach uses a simple yet effective rule-based fallback layer that performs sanity checks on an ML planner's decisions (e. g. avoiding collision, assuring physical feasibility).
Autonomy 2.0: Why is self-driving always 5 years away?
Despite the numerous successes of machine learning over the past decade (image recognition, decision-making, NLP, image synthesis), self-driving technology has not yet followed the same trend.
One Thousand and One Hours: Self-driving Motion Prediction Dataset
Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1, 000 hours of data.
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
We present PointFusion, a generic 3D object detection method that leverages both image and 3D point cloud information.
Learning Preferences for Manipulation Tasks from Online Coactive Feedback
We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots.
Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture
We introduce a diverse data set with 1180 miles of natural freeway and city driving, and show that we can anticipate maneuvers 3. 5 seconds before they occur in real-time with a precision and recall of 90. 5\% and 87. 4\% respectively.
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps.
Ranked #4 on
Skeleton Based Action Recognition
on CAD-120
Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture
We introduce a sensory-fusion architecture which jointly learns to anticipate and fuse information from multiple sensory streams.
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
We evaluate our approach on a diverse data set with 1180 miles of natural freeway and city driving and show that we can anticipate maneuvers 3. 5 seconds before they occur with over 80\% F1-score in real-time.
RoboBrain: Large-Scale Knowledge Engine for Robots
In this paper we introduce a knowledge engine, which learns and shares knowledge representations, for robots to carry out a variety of tasks.
PlanIt: A Crowdsourcing Approach for Learning to Plan Paths from Large Scale Preference Feedback
We represent trajectory preferences using a cost function that the robot learns and uses it to generate good trajectories in new environments.
Learning Trajectory Preferences for Manipulators via Iterative Improvement
In this paper, we propose a co-active online learning framework for teaching robots the preferences of its users for object manipulation tasks.
