8 accepted papers
We introduce Hand-Objectemph{(HO)GraspFlow}, an affordance-centric approach that retargets a single RGB with hand-object interaction (HOI) into multi-modal executable parallel jaw grasps without explicit geometric priors on target objects. Building on existing learning-based hand reconstruction and …
In this paper, we address the point cloud registration problem, where well-known methods like ICP fail under uncertainty arising from sensor noise, pose‐estimation errors, and partial overlap due to occlusion. We develop a novel approach, Gaussian Process Concept Attribution (GP-CA), which not only …
Preference-based reinforcement learning (PbRL) learns policies from human trajectory-level comparisons, avoiding explicit reward design and expert demonstrations. Existing methods typically train utility functions on trajectory or segment-level preferences while relying on per-step utility estimates…
Learning-based robotic grasping systems typically rely on large-scale datasets for training. However, collecting such datasets in the real-world is both costly and time-consuming. Synthetic data generation data is a cost-effective alternative, but models trained solely on synthetic data often strugg
We propose VISO-Grasp, a novel vision-language-informed system designed to systematically address visibility constraints for grasping in severely occluded environments. By leveraging Foundation Models (FMs) for spatial reasoning and active view planning, our framework constructs and updates an insta
Grasp learning in noisy environments, such as occlusions, sensor noise, and out-of-distribution (OOD) objects, poses significant challenges. Recent learning-based approaches focus primarily on capturing aleatoric uncertainty from inherent data noise. The epistemic uncertainty, which represents the O
We propose a novel hierarchical online learning scheme for fast and efficient bootstrapping of sensorimotor skills. Our scheme permits rapid data-driven robot model learning in a "learning while behaving" fashion. It is updated continuously to adapt to time-dependent changes and driven by an intrins…
When learning forward and inverse kinematics maps of manipulators, usually little attention is paid to data-efficiency, i.e., the accuracy gained per action-outcome sample. This paper examines properties of popular (online) learning techniques and demonstrates that - regardless of the employed explo…