How do humans navigate to a target object in an unmapped, unseen environment? We certainly do not wander aimlessly. Instead, human explorers naturally rely on spatial context, leveraging the inherent co-occurrence of everyday objects to infer a target’s probable location. However, conventional zero-shot object navigation methods construct a value map using only the target object, directing the agent toward the frontier with the highest score. This paper proposes a framework that leverages contextual cues from a Large Language Model (LLM). The proposed method uses an LLM to infer co-occurring objects near the target object. Based on current observations, the exploration direction is toward the frontiers where both the target and its co-occurring objects are most likely to be found. To compute the similarity between the target object and co-occurring objects, we designed value map fusion methods using static, dynamic, and adaptive structures. We verified the generalizability of our approach by integrating it into existing value-map based zero-shot object navigation models. Evaluations on the HM3D dataset demonstrated that the proposed method improves the Success Rate and the Success weighted by Path Length relative to the baselines. Therefore, mimicking human reasoning processes with LLM-provided contextual cues, we successfully enhanced zero-shot object navigation performance.
Sangmin Park, Minhwan Ko, Kyoobin Lee· 2026 23rd International Conf...· 0 citations
Vision-Language-Action (VLA) models have demonstrated strong performance in robot manipulation by leveraging pre-trained vision-language models to map observations directly to actions. However, existing approaches reason primarily at the visual or semantic level, lacking explicit understanding of the physical interactions that fundamentally govern manipulation tasks. In this paper, we propose Physics Reasoning VLA, a method that enables VLA models to explicitly reason about physical interactions prior to acting, grounded in two fundamental quantities: contact points, which specify where the target object interacts with the robot or surrounding environment, and contact forces, which describe the magnitude and direction of force applied at those locations. Rather than directly mapping observations to actions, our model first predicts contact points and forces at the pixel level via learnable physics queries, then incorporates the resulting physics-aware features alongside visual and language inputs to generate actions. To prevent physics reasoning from disrupting pre-trained visual and linguistic representations, we further introduce a hybrid attention mechanism that applies full attention over image, language, and proprioceptive tokens, while applying causal attention over physics query and action tokens. We evaluate our method on the RoboCasa simulation benchmark, demonstrating that physics reasoning consistently improves performance over the vanilla π0 baseline, with an average success rate improvement from 36.2% to 43.6%.
Kangmin Kim, Geonhyup Lee, Sangbeom Lee et al.· 2026 23rd International Conf...· 0 citations
