GRIM: Task-Oriented Grasping with Conditioning on Generative Examples

Task-Oriented Grasping (TOG) presents a significant challenge, requiring a nuanced understanding of task semantics, object affordances, and the functional constraints dictating how an object should be grasped for a specific task. To address these challenges, we introduce GRIM (Grasp Re-alignment via Iterative Matching), a novel training-free framework for task-oriented grasping. Initially, a coarse alignment strategy is developed using a combination of geometric cues and principal component analysis (PCA)-reduced DINO features for similarity scoring. Subsequently, the full grasp pose associated with the retrieved memory instance is transferred to the aligned scene object and further refined against a set of task-agnostic, geometrically stable grasps generated for the scene object, prioritizing task compatibility. In contrast to existing learning-based methods, GRIM demonstrates strong generalization capabilities, achieving robust performance with only a small number of conditioning examples.

Search-TTA: A Multimodal Test-Time Adaptation Framework for Visual Search in the Wild

To perform autonomous visual search for environmental monitoring, a robot may leverage satellite imagery as a prior map. This can help inform coarse, high-level search and exploration strategies, even when such images lack sufficient resolution to allow fine-grained, explicit visual recognition of targets. However, there are some challenges to overcome with using satellite images to direct visual search. For one, targets that are unseen in satellite images are underrepresented (compared to ground images) in most existing datasets, and thus vision models trained on these datasets fail to reason effectively based on indirect visual cues. Furthermore, approaches which leverage large Vision Language Models (VLMs) for generalization may yield inaccurate outputs due to hallucination, leading to inefficient search. To address these challenges, we introduce Search-TTA, a multimodal test-time adaptation framework that can accept text and/or image input. First, we pretrain a remote sensing image encoder to align with CLIP's visual encoder to output probability distributions of target presence used for visual search. Second, our framework dynamically refines CLIP's predictions during search using a test-time adaptation mechanism. Through a feedback loop inspired by Spatial Poisson Point Processes, gradient updates (weighted by uncertainty) are used to correct (potentially inaccurate) predictions and improve search performance. To validate Search-TTA's performance, we curate a visual search dataset based on internet-scale ecological data. We find that Search-TTA improves planner performance by up to 9.7%, particularly in cases with poor initial CLIP predictions. It also achieves comparable performance to state-of-the-art VLMs. Finally, we deploy Search-TTA on a real UAV via hardware-in-the-loop testing, by simulating its operation within a large-scale simulation that provides onboard sensing.

Evolution and the structure of learning agents

This paper presents the thesis that all learning agents of finite information size are limited by their informational structure in what goals they can efficiently learn to achieve in a complex environment. Evolutionary change is critical for creating the required structure for all learning agents in any complex environment. The thesis implies that there is no efficient universal learning algorithm. An agent can go past the learning limits imposed by its structure only by slow evolutionary change or blind search which in a very complex environment can only give an agent an inefficient universal learning capability that can work only in evolutionary timescales or improbable luck.