5 accepted papers
Collision-free robotic manipulation is extremely important for all safety-critical applications of robots. Especially for large-scale automation in modern manufacturing facilities where numerous hardware and software systems collaborate in relatively structured environments, accomplishing effectiven
Large language models (LLMs) represent a significant advancement in integrating physical robots with AI-driven systems. In this research, we present a framework that leverages Robotics Decision-Making Models (RDMM) for decision-making in domain-specific contexts, enhancing robotic autonomy. This fra
Partially Observable Markov Decision Processes (POMDPs) provide a structured framework for decision-making under uncertainty, but their application requires efficient belief updates. Sequential Importance Resampling Particle Filters (SIRPF), also known as Bootstrap Particle Filters, are commonly use
In robot task planning, large language models (LLMs) have shown significant promise in generating complex and long-horizon action sequences. However, it is observed that LLMs often produce responses that sound plausible but are not accurate. To address these problems, existing methods typically empl
Quality Estimation (QE) models have the potential to change how we evaluate and maybe even train machine translation models. However, these models still lack the robustness to achieve general adoption. We show that Stateof-the-art QE models, when tested in a Parallel Corpus Mining (PCM) setting, per…