We address the problem of autonomously learning controllers for vision-capable mobile robots. We extend McCallum's (1995) Nearest-Sequence Memory algorithm to allow for general metrics over state-action trajectories. We demonstrate the feasibility of ou
Existing work on Deep reinforcement learning-based visual navigation mainly focuses on autonomous ag...
Navigation is the fundamental capability of mobile robots which allows them to move fromone point to...
Part 1: Computer Vision and RoboticsInternational audienceThis paper presents a mobile control syste...
We address the problem of autonomously learning controllers for vision-capable mo...
Robot learning such as reinforcement learning gener-ally needs a well-defined state space in order t...
Robot learning such as reinforcement learning gen-erally needs a well-defined state space in order t...
Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in...
A key component of any reinforcement learning algorithm is the underlying representation used by the...
Reinforcement learning systems improve behaviour based on scalar rewards from a critic. In this work...
This paper presents a new reinforcement learning algorithm for accelerating acquisition of new skill...
In this paper, we present a novel vision-based learning approach for autonomous robot navigation. A ...
Abstract — Cameras are popular sensors for robot navigation tasks such as localization as they are i...
Abstract: This paper focuses on two issues on learning and development; a problem of state-action sp...
We present a provably near-optimal algorithm for reinforcement learn-ing in Markov decision processe...
Sensor and motor systems are not separable for autonomous agents to accomplish tasks in a dynamic en...
Existing work on Deep reinforcement learning-based visual navigation mainly focuses on autonomous ag...
Navigation is the fundamental capability of mobile robots which allows them to move fromone point to...
Part 1: Computer Vision and RoboticsInternational audienceThis paper presents a mobile control syste...
We address the problem of autonomously learning controllers for vision-capable mo...
Robot learning such as reinforcement learning gener-ally needs a well-defined state space in order t...
Robot learning such as reinforcement learning gen-erally needs a well-defined state space in order t...
Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in...
A key component of any reinforcement learning algorithm is the underlying representation used by the...
Reinforcement learning systems improve behaviour based on scalar rewards from a critic. In this work...
This paper presents a new reinforcement learning algorithm for accelerating acquisition of new skill...
In this paper, we present a novel vision-based learning approach for autonomous robot navigation. A ...
Abstract — Cameras are popular sensors for robot navigation tasks such as localization as they are i...
Abstract: This paper focuses on two issues on learning and development; a problem of state-action sp...
We present a provably near-optimal algorithm for reinforcement learn-ing in Markov decision processe...
Sensor and motor systems are not separable for autonomous agents to accomplish tasks in a dynamic en...
Existing work on Deep reinforcement learning-based visual navigation mainly focuses on autonomous ag...
Navigation is the fundamental capability of mobile robots which allows them to move fromone point to...
Part 1: Computer Vision and RoboticsInternational audienceThis paper presents a mobile control syste...