Raghav Anand — accepted AI-conference papers

2021

Serverless Multi-Query Motion Planning for Fog Robotics

Raghav Anand, Jeffrey Ichnowski, Chenggang Wu, Joseph M. Hellerstein, Joseph E. Gonzalez, Ken Goldberg

ICRA 2021poster

Robots in semi-structured environments such as homes and warehouses sporadically require computation of high-dimensional motion plans. Cloud and fog-based parallelization of motion planning can speed up planning. This can be further made efficient by the use of "serverless" on-demand computing as op…

Cited by 13 Source ↗Scholar ↗

2020

Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos

Ajay Kumar Tanwani, Pierre Sermanet, Andy Yan, Raghav Anand, Mariano Phielipp, Ken Goldberg

ICRA 2020poster

Learning meaningful visual representations in an embedding space can facilitate generalization in downstream tasks such as action segmentation and imitation. In this paper, we learn a motion-centric representation of surgical video demonstrations by grouping them into action segments/subgoals/option…

Cited by 57 Source ↗Scholar ↗

2020

RILaaS: Robot Inference and Learning as a Service

Ajay Kumar Tanwani, Raghav Anand, Joseph E. Gonzalez, Ken Goldberg

RA-L 2020

Programming robots is complicated due to the lack of `plug-and-play' modules for skill acquisition. Virtualizing deployment of deep learning models can facilitate large-scale use/re-use of off-the-shelf functional behaviors. Deploying deep learning models on robots entails real-time, accurate and re

Cited by 34 Source ↗Scholar ↗