6 papers
DISEIL: Demonstration Distillation for Sample-Efficient Imitation Learning
Suyog Khanal, Arun Kumar A, Santu Rana
A robot that can be taught a new task from a handful of demonstrations has to work out for itself what it still cannot do, and then ask for exactly that. Interactive imitation lear…
Leveraging Human Feedback for Semantically-Relevant Skill Discovery
Maxence Hussonnois, Thommen George Karimpanal, Santu Rana
Unsupervised skill discovery in reinforcement learning aims to intrinsically motivate agents to discover diverse and useful behaviours. However, unconstrained approaches can produc…
ASPECT:Analogical Semantic Policy Execution via Language Conditioned Transfer
Ajsal Shereef Palattuparambil, Thommen George Karimpanal, Santu Rana
Reinforcement Learning (RL) agents often struggle to generalize knowledge to new tasks, even those structurally similar to ones they have mastered. Although recent approaches have…
MAGIK: Mapping to Analogous Goals via Imagination-enabled Knowledge Transfer
Ajsal Shereef Palattuparambil, Thommen George Karimpanal, Santu Rana
Humans excel at analogical reasoning - applying knowledge from one task to a related one with minimal relearning. In contrast, reinforcement learning (RL) agents typically require…
Human-Aligned Skill Discovery: Balancing Behaviour Exploration and Alignment
Maxence Hussonnois, Thommen George Karimpanal, Santu Rana
Unsupervised skill discovery in Reinforcement Learning aims to mimic humans' ability to autonomously discover diverse behaviors. However, existing methods are often unconstrained,…
Dynamic Policy Fusion for User Alignment Without Re-Interaction
Ajsal Shereef Palattuparambil, Thommen George Karimpanal, Santu Rana
Deep reinforcement learning (RL) policies, although optimal in terms of task rewards, may not align with the personal preferences of human users. To ensure this alignment, a naive…