Embodied Intelligence

An Exploration of Embodied Visual Exploration
Santhosh K Ramakrishnan*, Dinesh Jayaraman, Kristen Grauman
An Exploration of Embodied Visual Exploration
Emergence of Exploratory Look-Around Behaviors Through Active Observation Completion
Santhosh K Ramakrishnan*, Dinesh Jayaraman, Kristen Grauman
End-to-End Policy Learning For Active Visual Categorization
Active visual perception with realistic and complex imagery can be formulated as an end-to-end reinforcement learning problem, the solution to which benefits from additionally exploiting the auxiliary task of action-conditioned future prediction.
Dinesh Jayaraman, Kristen Grauman
Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks
Task-agnostic visual exploration policies may be trained through a proxy “observation completion” task that requires an agent to “paint” unobserved views given a small set of observed views.
Dinesh Jayaraman, Kristen Grauman
Embodied Learning for Visual Recognition
Dinesh Jayaraman
Learning Image Representations Tied to Egomotion from Unlabeled Video
An agent’s continuous visual observations include information about how the world responds to its actions. This can provide an effective source of self-supervision for learning visual representations.
Dinesh Jayaraman, Kristen Grauman
Look-Ahead Before You Leap: End-to-End Active Recognition By Forecasting the Effect of Motion
Active visual perception with realistic and complex imagery can be formulated as an end-to-end reinforcement learning problem, the solution to which benefits from additionally exploiting the auxiliary task of action-conditioned future prediction.
Dinesh Jayaraman, Kristen Grauman
Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video
Assuming a world that mostly changes smoothly, continuous video streams entail implicit supervision that can be effectively exploited for learning visual representations.
Dinesh Jayaraman, Kristen Grauman
Learning Image Representations Tied to Egomotion
An agent’s continuous visual observations include information about how the world responds to its actions. This can provide an effective source of self-supervision for learning visual representations.
Dinesh Jayaraman, Kristen Grauman