41 research outputs found
Supervised Remote Robot with Guided Autonomy and Teleoperation (SURROGATE): A Framework for Whole-Body Manipulation
The use of the cognitive capabilities of humans to help guide the autonomy of robotics platforms in what is typically called “supervised-autonomy” is becoming more commonplace in robotics research. The work discussed in this paper presents an approach to a human-in-the-loop mode of robot operation that integrates high level human cognition and commanding with the intelligence and processing power of autonomous systems. Our framework for a “Supervised Remote Robot with Guided Autonomy and Teleoperation” (SURROGATE) is demonstrated on a robotic platform consisting of a pan-tilt perception head, two 7-DOF arms connected by a single 7-DOF torso, mounted on a tracked-wheel base. We present an architecture that allows high-level supervisory commands and intents to be specified by a user that are then interpreted by the robotic system to perform whole body manipulation tasks autonomously. We use a concept of “behaviors” to chain together sequences of “actions” for the robot to perform which is then executed real time
Bayesian filtering over compressed appearance states
This paper presents a framework for performing real-time recursive estimation of landmarks’ visual appearance. Imaging data in its original high dimensional space is probabilistically mapped to a compressed low dimensional space through the definition of likelihood functions. The likelihoods are subsequently fused with prior information using a Bayesian update. This process produces a probabilistic estimate of the low dimensional representation of the landmark visual appearance. The overall filtering provides information complementary to the conventional position estimates which is used to enhance data association. In addition to robotics observations, the filter integrates human observations in the appearance estimates. The appearance tracks as computed by the filter allow landmark classification. The set of labels involved in the classification task is thought of as an observation space where human observations are made by selecting a label. The low dimensional appearance estimates returned by the filter allow for low cost communication in low bandwidth sensor networks. Deployment of the filter in such a network is demonstrated in an outdoor mapping application involving a human operator, a ground and an air vehicle
Narrowing the Coordinate-frame Gap in Behavior Prediction Models: Distillation for Efficient and Accurate Scene-centric Motion Forecasting
Behavior prediction models have proliferated in recent years, especially in
the popular real-world robotics application of autonomous driving, where
representing the distribution over possible futures of moving agents is
essential for safe and comfortable motion planning. In these models, the choice
of coordinate frames to represent inputs and outputs has crucial trade offs
which broadly fall into one of two categories. Agent-centric models transform
inputs and perform inference in agent-centric coordinates. These models are
intrinsically invariant to translation and rotation between scene elements, are
best-performing on public leaderboards, but scale quadratically with the number
of agents and scene elements. Scene-centric models use a fixed coordinate
system to process all agents. This gives them the advantage of sharing
representations among all agents, offering efficient amortized inference
computation which scales linearly with the number of agents. However, these
models have to learn invariance to translation and rotation between scene
elements, and typically underperform agent-centric models. In this work, we
develop knowledge distillation techniques between probabilistic motion
forecasting models, and apply these techniques to close the gap in performance
between agent-centric and scene-centric models. This improves scene-centric
model performance by 13.2% on the public Argoverse benchmark, 7.8% on Waymo
Open Dataset and up to 9.4% on a large In-House dataset. These improved
scene-centric models rank highly in public leaderboards and are up to 15 times
more efficient than their agent-centric teacher counterparts in busy scenes.Comment: Accepted at ICRA 202
Cartographies d’orientations cristallines obtenues par série d’image ionique: technique iCHORD
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