Articulated objects pose diverse manipulation challenges for robots. Since their internal structures are not directly observable, robots must adaptively explore and refine actions to generate successful manipulation trajectories. While existing works have attempted cross-category generalization in adaptive articulated object manipulation, two major challenges persist: (1) the geometric diversity of real-world articulated objects complicates visual perception and understanding, and (2) variations in object functions and mechanisms hinder the development of a unified adaptive manipulation strategy. T o address these challenges, we propose AdaRPG, a novel framework that leverages foundation models to extract object parts, which exhibit greater local geometric similarity than entire objects, thereby enhancing visual affordance generalization for functional primitive skills. T o support this, we construct a part-level affordance annotation dataset to train the af-fordance model. Additionally, AdaRPG utilizes the common knowledge embedded in foundation models to reason about complex mechanisms and generate high-level control codes that invoke primitive skill functions based on part af-fordance inference. Simulation and real-world experiments demonstrate AdaRPG's strong generalization ability across novel articulated object categories.

Large language models (LLMs) showcase many desirable traits for intelligent and helpful robots. However, they are also known to hallucinate predictions. This issue is exacerbated in consumer robotics where LLM hallucinations may result in robots confidently executing plans that are contrary to user goals, relying more frequently on human assistance, or preventing the robot from asking for help at all. In this work, we present LAP, a novel approach for utilizing off-the-shelf LLM's, alongside scene and object Affordances, in robotic Planners that minimize harmful hallucinations and know when to ask for help. Our key finding is that calculating and leveraging a scene affordance score, a measure of whether a given action is possible in the provided scene, helps to mitigate hallucinations in LLM predictions and better align the LLM's confidence measure with the probability of success. We specifically propose and test three different affordance scores, which can be used independently or in tandem to improve performance across different use cases. The most successful of these individual scores involves prompting an LLM to determine if a given action is possible and safe in the given scene and uses the LLM's response to compute the score. Through experiments in both simulation and the real world, on tasks with a variety of ambiguities, we show that LAP significantly increases success rate and decreases the amount of human intervention required relative to prior art. For example, in our real-world testing paradigm, LAP decreases the human help rate of previous methods by over 33% at a success rate of 70%.