9 accepted papers
Underwater tactile sensing is critical for marine robots to reliably manipulate objects. However, harsh underwater environments bring in serious disturbances to sensor techniques. Prior studies have typically been restricted to on-land scenarios, 1D force measurements, or qualitative object property
Generalization in embodied AI is hindered by the "seeing-to-doing gap", stemming from data scarcity and embodiment heterogeneity. To address this, we pioneer "pointing" as a unified, embodiment-agnostic intermediate representation, defining four core embodied pointing abilities that bridge high-leve…
Achieving generalization in robotic manipulation remains a critical challenge, particularly for unseen scenarios and novel tasks. Current Vision-Language-Action (VLA) models, while building on top of general Vision-Language Models (VLMs), still fall short of achieving robust zero-shot performance du…
In inference-time scaling, Chain-of-Thought (CoT) plays a crucial role in enabling large language models (LLMs) to exhibit reasoning capabilities. However, in many scenarios, high-quality CoT data is scarce or even unavailable. In such cases, STaR-like methods can help LLMs synthesize CoT based on u…
Recent advances in Large Language Models (LLMs) have reshaped the landscape of reasoning tasks, particularly through test-time scaling (TTS) to enhance LLM reasoning. Prior research has used structures such as trees or graphs to guide LLMs in searching for optimal solutions. These methods are time-c…
Instruction tuning is widely used to unlock the abilities of Large Language Models (LLMs) in following human instructions, resulting in substantial performance improvements across various downstream tasks.Furthermore, contrastive decoding methods are employed to enhance instruction-tuned models. To…
As we all know, hallucinations prevail in Large Language Models (LLMs), where the generated content is coherent but factually incorrect, which inflicts a heavy blow on the widespread application of LLMs. Previous studies have shown that LLMs could confidently state non-existent facts rather than ans…
Recent studies in Retrieval-Augmented Generation (RAG) have investigated extracting evidence from retrieved passages to reduce computational costs and enhance the final RAG performance, yet it remains challenging. Existing methods heavily rely on heuristic-based augmentation, encountering several is…
Recent studies have explored the working mechanisms of In-Context Learning (ICL). However, they mainly focus on classification and simple generation tasks, limiting their broader application to more complex generation tasks in practice. To address this gap, we investigate the impact of demonstration…