Search Results for author:
Found 15 papers, 5 papers with code
Learning Goal-Conditioned Value Functions with one-step Path rewards rather than Goal-Rewards
Multi-goal reinforcement learning (MGRL) addresses tasks where the desired goal state can change for every trial.
FogGuard: guarding YOLO against fog using perceptual loss
In this paper, we present a novel fog-aware object detection network called FogGuard, designed to address the challenges posed by foggy weather conditions.
Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning
The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert's observations and state-control trajectory.
Control Barriers in Bayesian Learning of System Dynamics
This paper focuses on learning a model of system dynamics online while satisfying safety constraints.
OrcVIO: Object residual constrained Visual-Inertial Odometry
Introducing object-level semantic information into simultaneous localization and mapping (SLAM) system is critical.
Learning Navigation Costs from Demonstration with Semantic Observations
The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert's observations and state-control trajectory.
Learning Navigation Costs from Demonstrations with Semantic Observations
The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert’s observations and state-control trajectory.
Learning Navigation Costs from Demonstration in Partially Observable Environments
This paper focuses on inverse reinforcement learning (IRL) to enable safe and efficient autonomous navigation in unknown partially observable environments.
Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics
This paper focuses on learning a model of system dynamics online while satisfying safety constraints. Our motivation is to avoid offline system identification or hand-specified dynamics models and allowa system to safely and autonomously estimate and adapt its own model during online operation. Given streaming observations of the system state, we use Bayesian learning to obtain a distributionover the system dynamics.
Learning from Interventions using Hierarchical Policies for Safe Learning
The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer.
Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals
We do this by adapting the Floyd-Warshall algorithm for RL and call the adaptation Floyd-Warshall RL (FWRL).
A Critical Investigation of Deep Reinforcement Learning for Navigation
However, when trained and tested on different sets of maps, the algorithm fails to transfer the ability to gather and exploit map-information to unseen maps.
Do Deep Reinforcement Learning Algorithms really Learn to Navigate?
As the title of the paper by Mirowski et al. (2016) suggests, one might assume that DRL-based algorithms are able to “learn to navigate” and are thus ready to replace classical mapping and path-planning algorithms, at least in simulated environments.
A Continuous Occlusion Model for Road Scene Understanding
We present a physically interpretable, continuous 3D model for handling occlusions with applications to road scene understanding.
Spatiotemporal Articulated Models for Dynamic SLAM
We propose an online spatiotemporal articulation model estimation framework that estimates both articulated structure as well as a temporal prediction model solely using passive observations.
