6 papers
Can Causal Models Enhance Robot Navigation? Online Causal Adaptation for Real-Robot Navigation
Zhitao Liang, Alex Mitrevski, Emmanuel Dean +1
Causality in robotics aims to produce more interpretable and flexible robot behaviours by enabling robots to predict the consequences of their actions; however, deploying causal mo…
Evaluating Generative Models as Interactive Emergent Representations of Human-Like Collaborative Behavior
Shinas Shaji, Teena Chakkalayil Hassan, Sebastian Houben +1
Human-AI collaboration requires AI agents to understand human behavior for effective coordination. While advances in foundation models show promising capabilities in understanding…
COFFAIL: A Dataset of Successful and Anomalous Robot Skill Executions in the Context of Coffee Preparation
Alex Mitrevski, Ayush Salunke
In the context of robot learning for manipulation, curated datasets are an important resource for advancing the state of the art; however, available datasets typically only include…
From Language to Action: Can LLM-Based Agents Be Used for Embodied Robot Cognition?
Shinas Shaji, Fabian Huppertz, Alex Mitrevski +1
In order to flexibly act in an everyday environment, a robotic agent needs a variety of cognitive capabilities that enable it to reason about plans and perform execution recovery.…
Reliable Robotic Task Execution in the Face of Anomalies
Bharath Santhanam, Alex Mitrevski, Santosh Thoduka +2
Learned robot policies have consistently been shown to be versatile, but they typically have no built-in mechanism for handling the complexity of open environments, making them pro…
Are Learning-Based Approaches Ready for Real-World Indoor Navigation? A Case for Imitation Learning
Nigitha Selvaraj, Alex Mitrevski, Sebastian Houben
Traditional indoor robot navigation methods provide a reliable solution when adapted to constrained scenarios, but lack flexibility or require manual re-tuning when deployed in mor…