11 accepted papers
We present EWareNet, a novel intent and affect-aware social robot navigation algorithm among pedestrians. Our approach predicts the trajectory-based pedestrian intent from gait sequence, which is then used for intent-guided navigation taking into account social and proxemic constraints. We propose a…
Recent advances in 3D object detection (3DOD) have obtained remarkably strong results for LiDAR-based models. In contrast, surround-view 3DOD models based on multiple camera images underperform due to the necessary view transformation of features from perspective view (PV) to a 3D world representati…
Panoptic Segmentation aims to provide an understanding of background (stuff) and instances of objects (things) at a pixel level. It combines the separate tasks of semantic segmentation (pixel level classification) and instance segmentation to build a single unified scene understanding task. Typicall…
We present ProxEmo, a novel end-to-end emotion prediction algorithm for socially aware robot navigation among pedestrians. Our approach predicts the perceived emotions of a pedestrian from walking gaits, which is then used for emotion-guided navigation taking into account social and proxemic constra…
Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects. In this work, we introduce PoseCNN, a new Convolutional Neu…
A fundamental robot perception task is that of identifying and estimating the poses of objects with known 3D models in RGB-D data. While feature-based and discriminative approaches have been traditionally used for this task, recent work on deliberative approaches such as PERCH and D2P have shown imp…
The benefits of bidirectional planning over the unidirectional version are well established for motion planning in high-dimensional configuration spaces. While bidirectional approaches have been employed with great success in the context of sampling-based planners such as in RRT-Connect, they have n
We introduce a novel paradigm for model-based multi-object recognition and 3 DoF pose estimation from 3D sensor data that integrates exhaustive global reasoning with discriminatively-trained algorithms in a principled fashion. Typ- ical approaches for this task are based on scene-to-model feature ma…
In many robotic domains such as flexible automated manufacturing or personal assistance, a fundamental perception task is that of identifying and localizing objects whose 3D models are known. Canonical approaches to this problem include discriminative methods that find correspondences between featur
Many motion planning problems in robotics are high dimensional planning problems. While sampling-based motion planning algorithms handle the high dimensionality very well, the solution qualities are often hard to control due to the inherent randomization. In addition, they suffer severely when the c…
Personal robots need to manipulate a variety of articulated mechanisms as part of day-to-day tasks. These tasks are often specific, goal-driven, and permit very little bootstrap time for learning the articulation type. In this work, we address the problem of purposefully manipulating an articulated…