Search Results for author: Ben Talbot

Found 9 papers, 2 papers with code

Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots

no code implementations10 Dec 2021 Krishan Rana, Vibhavari Dasagi, Jesse Haviland, Ben Talbot, Michael Milford, Niko Sünderhauf

While deep reinforcement learning (RL) agents have demonstrated incredible potential in attaining dexterous behaviours for robotics, they tend to make errors when deployed in the real world due to mismatches between the training and execution environments.

Continuous Control

Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics

no code implementations21 Jul 2021 Krishan Rana, Vibhavari Dasagi, Jesse Haviland, Ben Talbot, Michael Milford, Niko Sünderhauf

More importantly, given the risk-aversity of the control prior, BCF ensures safe exploration and deployment, where the control prior naturally dominates the action distribution in states unknown to the policy.

reinforcement-learning reinforcement Learning +1

Learning and Executing Re-usable Behaviour Trees from Natural Language Instruction

1 code implementation3 Jun 2021 Gavin Suddrey, Ben Talbot, Frederic Maire

In this paper we demonstrate how behaviour trees, a well established control architecture in the fields of gaming and robotics, can be used in conjunction with natural language instruction to provide a robust and modular control architecture for instructing autonomous agents to learn and perform novel complex tasks.

BenchBot: Evaluating Robotics Research in Photorealistic 3D Simulation and on Real Robots

no code implementations3 Aug 2020 Ben Talbot, David Hall, Haoyang Zhang, Suman Raj Bista, Rohan Smith, Feras Dayoub, Niko Sünderhauf

We introduce BenchBot, a novel software suite for benchmarking the performance of robotics research across both photorealistic 3D simulations and real robot platforms.

Robotics

Multiplicative Controller Fusion: Leveraging Algorithmic Priors for Sample-efficient Reinforcement Learning and Safe Sim-To-Real Transfer

1 code implementation11 Mar 2020 Krishan Rana, Vibhavari Dasagi, Ben Talbot, Michael Milford, Niko Sünderhauf

We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment.

Robot Navigation

Robot Navigation in Unseen Spaces using an Abstract Map

no code implementations31 Jan 2020 Ben Talbot, Feras Dayoub, Peter Corke, Gordon Wyeth

Symbolic navigation performance of humans and a robot is evaluated in a real-world built environment.

Navigate Robot Navigation

Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments

no code implementations24 Sep 2019 Krishan Rana, Ben Talbot, Vibhavari Dasagi, Michael Milford, Niko Sünderhauf

In this work we focus on improving the efficiency and generalisation of learned navigation strategies when transferred from its training environment to previously unseen ones.

OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions

no code implementations6 Apr 2018 Ben Talbot, Sourav Garg, Michael Milford

Visually recognising a traversed route - regardless of whether seen during the day or night, in clear or inclement conditions, or in summer or winter - is an important capability for navigating robots.

Visual Place Recognition

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