103 citations · 152 across the 16 of their papers we have counts for
17 papers
SG-CoT: An Ambiguity-Aware Robotic Planning Framework using Scene Graph Representations
Akshat Rana, Peeyush Agarwal, K. P. S. Rana +1
Ambiguity poses a major challenge to large language models (LLMs) used as robotic planners. In this letter, we present Scene Graph-Chain-of-Thought (SG-CoT), a two-stage framework…
Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin
Jad Abou-Chakra, Lingfeng Sun, Krishan Rana +5
We introduce real-is-sim, a new approach to integrating simulation into behavior cloning pipelines. In contrast to real-only methods, which lack the ability to safely test policies…
IMLE Policy: Fast and Sample Efficient Visuomotor Policy Learning via Implicit Maximum Likelihood Estimation
Krishan Rana, Robert Lee, David Pershouse +1
Recent advances in imitation learning, particularly using generative modelling techniques like diffusion, have enabled policies to capture complex multi-modal action distributions.…
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…
Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames
Krishan Rana, Jad Abou-Chakra, Sourav Garg +3
Imitation learning has unlocked the potential for robots to exhibit highly dexterous behaviours. However, it still struggles with long-horizon, multi-object tasks due to poor sampl…
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…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
Krishan Rana, Jesse Haviland, Sourav Garg +3
Large language models (LLMs) have demonstrated impressive results in developing generalist planning agents for diverse tasks. However, grounding these plans in expansive, multi-flo…
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…
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…
Critic Guided Segmentation of Rewarding Objects in First-Person Views
Andrew Melnik, Augustin Harter, Christian Limberg +3
This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset. For that, we train an Hourglass network…
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…
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…
Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments
Krishan Rana, Ben Talbot, Vibhavari Dasagi +2
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. We p…