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Found 13 papers, 4 papers with code
Probably Approximately Correct Vision-Based Planning using Motion Primitives
This paper presents an approach for learning vision-based planners that provably generalize to novel environments (i. e., environments unseen during training).
CoNES: Convex Natural Evolutionary Strategies
We present a novel algorithm -- convex natural evolutionary strategies (CoNES) -- for optimizing high-dimensional blackbox functions by leveraging tools from convex optimization and information geometry.
Generalization Guarantees for Imitation Learning
Control policies from imitation learning can often fail to generalize to novel environments due to imperfect demonstrations or the inability of imitation learning algorithms to accurately infer the expert's policies.
LagNetViP: A Lagrangian Neural Network for Video Prediction
In this paper, we introduce a video prediction model where the equations of motion are explicitly constructed from learned representations of the underlying physical quantities.
Task-Driven Detection of Distribution Shifts with Statistical Guarantees for Robot Learning
Our goal is to perform out-of-distribution (OOD) detection, i. e., to detect when a robot is operating in environments drawn from a different distribution than the ones used to train the robot.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Interactive Dynamic Walking: Learning Gait Switching Policies with Generalization Guarantees
In this paper, we consider the problem of adapting a dynamically walking bipedal robot to follow a leading co-worker while engaging in tasks that require physical interaction.
Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data
The key idea behind our approach is to utilize the generative model in order to implicitly specify a prior over policies.
Learning Provably Robust Motion Planners Using Funnel Libraries
This paper presents an approach for learning motion planners that are accompanied with probabilistic guarantees of success on new environments that hold uniformly for any disturbance to the robot's dynamics within an admissible set.
Guided Conditional Diffusion for Controllable Traffic Simulation
Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles.
Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors
Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios.
Interactive Joint Planning for Autonomous Vehicles
In particular, IJP jointly optimizes over the behavior of the ego and the surrounding agents and leverages deep-learned prediction models as prediction priors that the join trajectory optimization tries to stay close to.
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction
To remedy this, recent work has proposed learning model and score function parameters using data to directly optimize the efficiency of the ICP prediction sets.
Driving Everywhere with Large Language Model Policy Adaptation
Adapting driving behavior to new environments, customs, and laws is a long-standing problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs).
