5 accepted papers
Learning the dynamics of deformable objects, such as dough or a sponge, from RGB-D videos is challenging due to insufficient visual cues and complex deformations. We introduce PLOP (Particle Filtering for Learning Object Physics), a novel framework to learn the dynamics model of deformable objects u…
Cross-pose estimation between rigid objects is a fundamental building block for robotic applications. In this paper, we propose a new cross-pose estimation method that predicts correspondences on a set level as opposed to a point level. This contrasts methods that predict cross-pose from per-point c…
"Predicting futures of surrounding agents is critical for autonomous systems such as self-driving cars. Instead of requiring accurate detection and tracking prior to trajectory prediction, an object agnostic Sequential Pointcloud Forecasting (SPF) task was proposed in prior work, which enables a for…
Deep learning based object detectors often report false positives with very high confidence. Although they optimize generic detection performance, such as mean average precision (mAP), they are not designed for robustness or verifiability. We argue that, if a high confidence detection is made by a r
Inspired by the ability of animals to rely on proprioception and vestibular feedback to adapt their gait, we propose a modular framework for autonomous locomotion that relies on force sensing and inertial information. A first controller exploits anti-compliance, a new application of positive force f…