1 citations · 1 across the 6 of their papers we have counts for
6 papers
HDDLGym: A Tool for Studying Multi-Agent Hierarchical Problems Defined in HDDL with OpenAI Gym
Ngoc La, Ruaridh Mon-Williams, Julie A. Shah
In recent years, reinforcement learning (RL) methods have been widely tested using tools like OpenAI Gym, though many tasks in these environments could also benefit from hierarchic…
Partner Modelling Emerges in Recurrent Agents (But Only When It Matters)
Ruaridh Mon-Williams, Max Taylor-Davies, Elizabeth Mieczkowski +5
Humans are remarkably adept at collaboration, able to infer the strengths and weaknesses of new partners in order to work successfully towards shared goals. To build AI systems wit…
Predicting Multi-Agent Specialization via Task Parallelizability
Elizabeth Mieczkowski, Ruaridh Mon-Williams, Neil Bramley +3
When should we encourage specialization in multi-agent systems versus train generalists that perform the entire task independently? We propose that specialization largely depends o…
Learning Precise Affordances from Egocentric Videos for Robotic Manipulation
Gen Li, Nikolaos Tsagkas, Jifei Song +4
Affordance, defined as the potential actions that an object offers, is crucial for embodied AI agents. For example, such knowledge directs an agent to grasp a knife by the handle f…
Enabling robots to follow abstract instructions and complete complex dynamic tasks
Ruaridh Mon-Williams, Gen Li, Ran Long +2
Completing complex tasks in unpredictable settings like home kitchens challenges robotic systems. These challenges include interpreting high-level human commands, such as "make me…
A behavioural transformer for effective collaboration between a robot and a non-stationary human
Ruaridh Mon-Williams, Theodoros Stouraitis, Sethu Vijayakumar
A key challenge in human-robot collaboration is the non-stationarity created by humans due to changes in their behaviour. This alters environmental transitions and hinders human-ro…