Hung-Ting Su
Senior Associate Researcher, Delta Robotics Innovation Center, Delta Electronics
Ph.D. in Computer Science, National Taiwan University. Former visiting researcher at Columbia University.
I am a researcher-engineer building reliable multimodal AI systems for embodied and long-horizon decision-making, spanning real-world robot learning, tactile/visual policy learning, trustworthy LLM/MLLM reasoning, and multimodal evaluation.
Before joining DRIC, I completed my Ph.D. and later postdoctoral research at National Taiwan University under the supervision of Prof. Winston H. Hsu. During that time, I also collaborated closely with Prof. Min Sun, Prof. Hung-yi Lee, and Prof. Pu-Jen Cheng on trustworthy multimodal reasoning and embodied AI. I was also a visiting researcher at Columbia University, hosted by Prof. Shih-Fu Chang.
I am actively seeking full-time research or applied scientist/engineer roles, as well as postdoctoral opportunities.
Selected Publications
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VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions
Reliable embodied reasoning when instructions contain invalid assumptions.
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Unveiling Narrative Reasoning Limits of Large Language Models with Trope in Movie Synopses
Evaluating narrative reasoning limits and failure modes in LLMs.
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Context-Aware Replanning with Pre-Explored Semantic Map for Object Navigation
Semantic replanning for more robust embodied navigation.
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ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints
Benchmarking commonsense planning under underspecified affordance constraints.
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Affordance-Guided Coarse-to-Fine Exploration for Base Placement in Open-Vocabulary Mobile Manipulation
Affordance-aware exploration for mobile manipulation and base placement.
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MovieCORE: COgnitive REasoning in Movies
Long-form multimodal reasoning over movie narratives.
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HERMES: temporal-coHERent long-forM understanding with Episodes and Semantics
Temporal coherence and semantic structure for long-horizon movie understanding.
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AED: Adaptable Error Detection for Few-shot Imitation Policy
Monitoring and error detection for imitation policies in embodied settings.
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Research Interests
- Real-world robot learning and embodied AI
- Trustworthy LLM and MLLM reasoning
- Long-horizon multimodal understanding and evaluation
- Tactile/VLA-style manipulation, world models, and failure analysis
About
My work spans robot learning, trustworthy reasoning, and long-horizon multimodal understanding, with recent emphasis on tactile and visual policy learning, VLA-style manipulation, and failure-aware evaluation in real-world settings.
Across robotics, language, and vision, I study how multimodal systems gather evidence, revise beliefs, and remain reliable when observations are incomplete, ambiguous, or physically constrained.