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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.
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.
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.
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.
