103 citations · 275 across the 25 of their papers we have counts for
14 papers · 1 filter
Multi-Modal 3D Scene Graph Updater for Shared and Dynamic Environments
Emilio Olivastri, Jonathan Francis, Alberto Pretto +2
The advent of generalist Large Language Models (LLMs) and Large Vision Models (VLMs) have streamlined the construction of semantically enriched maps that can enable robots to groun…
Physically Embodied Gaussian Splatting: A Realtime Correctable World Model for Robotics
Jad Abou-Chakra, Krishan Rana, Feras Dayoub +1
For robots to robustly understand and interact with the physical world, it is highly beneficial to have a comprehensive representation - modelling geometry, physics, and visual obs…
RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation
Sourav Garg, Krishan Rana, Mehdi Hosseinzadeh +4
Mapping is crucial for spatial reasoning, planning and robot navigation. Existing approaches range from metric, which require precise geometry-based optimization, to purely topolog…
LHManip: A Dataset for Long-Horizon Language-Grounded Manipulation Tasks in Cluttered Tabletop Environments
Federico Ceola, Lorenzo Natale, Niko Sünderhauf +1
Instructing a robot to complete an everyday task within our homes has been a long-standing challenge for robotics. While recent progress in language-conditioned imitation learning…
Contrastive Language, Action, and State Pre-training for Robot Learning
Krishan Rana, Andrew Melnik, Niko Sünderhauf
In this paper, we introduce a method for unifying language, action, and state information in a shared embedding space to facilitate a range of downstream tasks in robot learning. O…
Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics
Krishan Rana, Ming Xu, Brendan Tidd +2
Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowledge for accelerated robot learning. Skills are typically extracted from expert d…
Implicit Object Mapping With Noisy Data
Jad Abou-Chakra, Feras Dayoub, Niko Sünderhauf
Modelling individual objects in a scene as Neural Radiance Fields (NeRFs) provides an alternative geometric scene representation that may benefit downstream robotics tasks such as…
Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots
Krishan Rana, Vibhavari Dasagi, Jesse Haviland +3
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 r…
Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM
Suman Raj Bista, David Hall, Ben Talbot +3
Recent Semantic SLAM methods combine classical geometry-based estimation with deep learning-based object detection or semantic segmentation. In this paper we evaluate the quality o…
Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness
Ming Xu, Tobias Fischer, Niko Sünderhauf +1
Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exter…
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics
Krishan Rana, Vibhavari Dasagi, Jesse Haviland +3
We present Bayesian Controller Fusion (BCF): a hybrid control strategy that combines the strengths of traditional hand-crafted controllers and model-free deep reinforcement learnin…
Semantics for Robotic Mapping, Perception and Interaction: A Survey
Sourav Garg, Niko Sünderhauf, Feras Dayoub +9
For robots to navigate and interact more richly with the world around them, they will likely require a deeper understanding of the world in which they operate. In robotics and rela…
BenchBot: Evaluating Robotics Research in Photorealistic 3D Simulation and on Real Robots
Ben Talbot, David Hall, Haoyang Zhang +4
We introduce BenchBot, a novel software suite for benchmarking the performance of robotics research across both photorealistic 3D simulations and real robot platforms. BenchBot pro…
Multiplicative Controller Fusion: Leveraging Algorithmic Priors for Sample-efficient Reinforcement Learning and Safe Sim-To-Real Transfer
Krishan Rana, Vibhavari Dasagi, Ben Talbot +2
Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intrac…