3 accepted papers
We introduce a simple framework for predicting the behavior of an agent in multi-agent settings. In contrast to autoregressive (AR) tasks, such as language processing, our focus is on scenarios with multiple agents whose interactions are shaped by physical constraints and internal motivations. To th…
We propose a modular systems architecture, MANIP, that can facilitate the design and development of robot manipulation systems by systematically combining learned subpolicies with well-established procedural algorithmic primitives such as Inverse Kinematics, Kalman Filters, RANSAC outlier rejection,…
Multi-object tracking (MOT) is a cornerstone capability of any robotic system. Tracking quality is largely dependent on the quality of input detections. In many applications, such as autonomous driving, it is preferable to over-detect objects to avoid catastrophic outcomes due to missed detections.