32 research outputs found
Symmetric Rank Covariances: a Generalised Framework for Nonparametric Measures of Dependence
The need to test whether two random vectors are independent has spawned a
large number of competing measures of dependence. We are interested in
nonparametric measures that are invariant under strictly increasing
transformations, such as Kendall's tau, Hoeffding's D, and the more recently
discovered Bergsma--Dassios sign covariance. Each of these measures exhibits
symmetries that are not readily apparent from their definitions. Making these
symmetries explicit, we define a new class of multivariate nonparametric
measures of dependence that we refer to as Symmetric Rank Covariances. This new
class generalises all of the above measures and leads naturally to multivariate
extensions of the Bergsma--Dassios sign covariance. Symmetric Rank Covariances
may be estimated unbiasedly using U-statistics for which we prove results on
computational efficiency and large-sample behavior. The algorithms we develop
for their computation include, to the best of our knowledge, the first
efficient algorithms for the well-known Hoeffding's D statistic in the
multivariate setting
Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics
A common assumption when training embodied agents is that the impact of
taking an action is stable; for instance, executing the "move ahead" action
will always move the agent forward by a fixed distance, perhaps with some small
amount of actuator-induced noise. This assumption is limiting; an agent may
encounter settings that dramatically alter the impact of actions: a move ahead
action on a wet floor may send the agent twice as far as it expects and using
the same action with a broken wheel might transform the expected translation
into a rotation. Instead of relying that the impact of an action stably
reflects its pre-defined semantic meaning, we propose to model the impact of
actions on-the-fly using latent embeddings. By combining these latent action
embeddings with a novel, transformer-based, policy head, we design an Action
Adaptive Policy (AAP). We evaluate our AAP on two challenging visual navigation
tasks in the AI2-THOR and Habitat environments and show that our AAP is highly
performant even when faced, at inference-time with missing actions and,
previously unseen, perturbed action space. Moreover, we observe significant
improvement in robustness against these actions when evaluating in real-world
scenarios.Comment: 21 pages, 17 figures, ICLR 202
AI2-THOR: An Interactive 3D Environment for Visual AI
We introduce The House Of inteRactions (THOR), a framework for visual AI
research, available at http://ai2thor.allenai.org. AI2-THOR consists of near
photo-realistic 3D indoor scenes, where AI agents can navigate in the scenes
and interact with objects to perform tasks. AI2-THOR enables research in many
different domains including but not limited to deep reinforcement learning,
imitation learning, learning by interaction, planning, visual question
answering, unsupervised representation learning, object detection and
segmentation, and learning models of cognition. The goal of AI2-THOR is to
facilitate building visually intelligent models and push the research forward
in this domain
Phone2Proc: Bringing Robust Robots Into Our Chaotic World
Training embodied agents in simulation has become mainstream for the embodied
AI community. However, these agents often struggle when deployed in the
physical world due to their inability to generalize to real-world environments.
In this paper, we present Phone2Proc, a method that uses a 10-minute phone scan
and conditional procedural generation to create a distribution of training
scenes that are semantically similar to the target environment. The generated
scenes are conditioned on the wall layout and arrangement of large objects from
the scan, while also sampling lighting, clutter, surface textures, and
instances of smaller objects with randomized placement and materials.
Leveraging just a simple RGB camera, training with Phone2Proc shows massive
improvements from 34.7% to 70.7% success rate in sim-to-real ObjectNav
performance across a test suite of over 200 trials in diverse real-world
environments, including homes, offices, and RoboTHOR. Furthermore, Phone2Proc's
diverse distribution of generated scenes makes agents remarkably robust to
changes in the real world, such as human movement, object rearrangement,
lighting changes, or clutter.Comment: https://allenai.org/project/phone2pro