activity
20172022
most citedEmergence of Locomotion Behaviours in Rich Environments

668 citations · 875 across the 13 of their papers we have counts for

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Showing cs.AIShow all

8 papers · 1 filter

cs.AI20205 cited

Probing Emergent Semantics in Predictive Agents via Question Answering

Abhishek Das, Federico Carnevale, Hamza Merzic +8

Recent work has shown how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments. We propose questio…

cs.AI2019

Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks

Josh Merel, Saran Tunyasuvunakool, Arun Ahuja +6

We address the longstanding challenge of producing flexible, realistic humanoid character controllers that can perform diverse whole-body tasks involving object interactions. This…

cs.AI2019

What can the brain teach us about building artificial intelligence?

Dileep George

This paper is the preprint of an invited commentary on Lake et al's Behavioral and Brain Sciences article titled "Building machines that learn and think like people". Lake et al's…

cs.AI2018

Hierarchical visuomotor control of humanoids

Josh Merel, Arun Ahuja, Vu Pham +5

We aim to build complex humanoid agents that integrate perception, motor control, and memory. In this work, we partly factor this problem into low-level motor control from proprioc…

cs.AI2018

Optimizing Agent Behavior over Long Time Scales by Transporting Value

Chia-Chun Hung, Timothy Lillicrap, Josh Abramson +5

Humans spend a remarkable fraction of waking life engaged in acts of "mental time travel". We dwell on our actions in the past and experience satisfaction or regret. More than mere…

cs.AI2018

Probing Physics Knowledge Using Tools from Developmental Psychology

Luis Piloto, Ari Weinstein, Dhruva TB +6

In order to build agents with a rich understanding of their environment, one key objective is to endow them with a grasp of intuitive physics; an ability to reason about three-dime…