5 citations · 17 across the 5 of their papers we have counts for
8 papers · 1 filter
Shaping embodied agent behavior with activity-context priors from egocentric video
Tushar Nagarajan, Kristen Grauman
Complex physical tasks entail a sequence of object interactions, each with its own preconditions -- which can be difficult for robotic agents to learn efficiently solely through th…
Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos
Yanghao Li, Tushar Nagarajan, Bo Xiong +1
We introduce an approach for pre-training egocentric video models using large-scale third-person video datasets. Learning from purely egocentric data is limited by low dataset scal…
Environment Predictive Coding for Embodied Agents
Santhosh K. Ramakrishnan, Tushar Nagarajan, Ziad Al-Halah +1
We introduce environment predictive coding, a self-supervised approach to learn environment-level representations for embodied agents. In contrast to prior work on self-supervised…
Learning Affordance Landscapes for Interaction Exploration in 3D Environments
Tushar Nagarajan, Kristen Grauman
Embodied agents operating in human spaces must be able to master how their environment works: what objects can the agent use, and how can it use them? We introduce a reinforcement…
EGO-TOPO: Environment Affordances from Egocentric Video
Tushar Nagarajan, Yanghao Li, Christoph Feichtenhofer +1
First-person video naturally brings the use of a physical environment to the forefront, since it shows the camera wearer interacting fluidly in a space based on his intentions. How…
Grounded Human-Object Interaction Hotspots from Video (Extended Abstract)
Tushar Nagarajan, Christoph Feichtenhofer, Kristen Grauman
Learning how to interact with objects is an important step towards embodied visual intelligence, but existing techniques suffer from heavy supervision or sensing requirements. We p…