6 accepted papers
My doctoral research develops a unified framework for offline imitation learning (IL) that tackles three central challenges: achieving sample efficiency in strictly batch settings, ensuring robustness and generalization under dynamics shifts, and learning from demonstrations of varying quality. At t
We introduce Language2Gesture (L2G), a cross-modal generative model designed to predict head gesture animations directly from audio inputs. Unlike existing head gesture prediction models, L2G excels in modelling both macro and subtle micro head gesture motions. L2G further refines the generated moti…
Imitation learning (IL) is notably effective for robotic tasks where directly programming behaviors or defining optimal control costs is challenging. In this work, we address a scenario where the imitator relies solely on observed behavior and cannot make environmental interactions during learning.…
We present SpecMAS, a novel multi-agent system that autonomously constructs and formally verifies executable system models from natural language specifications. Given a Standard Operating Procedure (SOP) describing a target system, SpecMAS parses the specification, identifies relevant operational mo…
AI systems’ ability to interpret human emotions and adapt to variations is becoming more crucial as AI gets embedded into everyone’s daily lives. Emotion Recognition in Conversations (ERC) is based on this fundamental challenge. Current state-of-the-art technologies in ERC are limited due to the nee…
Norms help regulate a society. Norms may be explicit (represented in structured form) or implicit. We address the emergence of explicit norms by developing agents who provide and reason about explanations for norm violations in deciding sanctions and identifying alternative norms. These agents use…