1 citations · 2 across the 11 of their papers we have counts for
12 papers
RobotUse: Allocating Computation, Context, and Decisions
Junhoo Lee, Injun Baek, Seungyeon Kim +4
Robot agents must connect their intended actions to observed outcomes while retaining the context needed to revise their choices over repeated attempts. Existing interfaces often l…
LandingAgent: A Reference-Annotated Dataset and Agentic Generation Framework for Landing Pages
Injun Baek, HyeongSeok Lee, Yearim Kim +2
Landing pages are goal-oriented web interfaces that must communicate a target-specific value proposition while organizing information flow, visual hierarchy, and calls to action (C…
When Saying No Makes Better Videos: Designing Dual Gatekeeping for Pedagogically Grounded AI Content Creation
Yearim Kim, Injun Baek, Nojun Kwak
To prevent the adoption of aesthetically polished but pedagogically flawed AI content, we study a video authoring pipeline featuring two layers of structured refusal. The first lay…
Multiverse: Language-Conditioned Multi-Game Level Blending via Shared Representation
In-Chang Baek, Jiyun Jung, Geum-Hwan Hwang +2
Text-to-level generation aims to translate natural language descriptions into structured game levels, enabling intuitive control over procedural content generation. While prior tex…
PedaCo-Gen: Scaffolding Pedagogical Agency in Human-AI Collaborative Video Authoring
Injun Baek, Yearim Kim, Nojun Kwak
While advancements in Text-to-Video (T2V) generative AI offer a promising path toward democratizing content creation, current models are often optimized for visual fidelity rather…
A Humanoid Visual-Tactile-Action Dataset for Contact-Rich Manipulation
Eunju Kwon, Seungwon Oh, In-Chang Baek +5
Contact-rich manipulation has become increasingly important in robot learning. However, previous studies on robot learning datasets have focused on rigid objects and underrepresent…
Human-Aligned Procedural Level Generation Reinforcement Learning via Text-Level-Sketch Shared Representation
In-Chang Baek, Seoyoung Lee, Sung-Hyun Kim +2
Human-aligned AI is a critical component of co-creativity, as it enables models to accurately interpret human intent and generate controllable outputs that align with design goals…
Multi-Objective Instruction-Aware Representation Learning in Procedural Content Generation RL
Sung-Hyun Kim, Geum-Hwan Hwang, In-Chang Baek +2
Recent advancements in generative modeling emphasize the importance of natural language as a highly expressive and accessible modality for controlling content generation. However,…
IPCGRL: Language-Instructed Reinforcement Learning for Procedural Level Generation
In-Chang Baek, Sung-Hyun Kim, Seo-Young Lee +2
Recent research has highlighted the significance of natural language in enhancing the controllability of generative models. While various efforts have been made to leverage natural…
PCGRLLM: Large Language Model-Driven Reward Design for Procedural Content Generation Reinforcement Learning
In-Chang Baek, Sung-Hyun Kim, Sam Earle +4
Reward design plays a pivotal role in the training of game AIs, requiring substantial domain-specific knowledge and human effort. In recent years, several studies have explored rew…
ChatPCG: Large Language Model-Driven Reward Design for Procedural Content Generation
In-Chang Baek, Tae-Hwa Park, Jin-Ha Noh +2
Driven by the rapid growth of machine learning, recent advances in game artificial intelligence (AI) have significantly impacted productivity across various gaming genres. Reward d…
RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games
Hyeon-Chang Jeon, In-Chang Baek, Cheong-mok Bae +7
The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Altho…