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Found 7 papers, 4 papers with code
Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World
Reinforcement learning (RL) with dense rewards and imitation learning (IL) with human-generated trajectories are the most widely used approaches for training modern embodied agents.
Scene Graph Contrastive Learning for Embodied Navigation
Training effective embodied AI agents often involves expert imitation, specialized components such as maps, or leveraging additional sensors for depth and localization.
A General Purpose Supervisory Signal for Embodied Agents
Training effective embodied AI agents often involves manual reward engineering, expert imitation, specialized components such as maps, or leveraging additional sensors for depth and localization.
Ask4Help: Learning to Leverage an Expert for Embodied Tasks
Embodied AI agents continue to become more capable every year with the advent of new models, environments, and benchmarks, but are still far away from being performant and reliable enough to be deployed in real, user-facing, applications.
BNAS v2: Learning Architectures for Binary Networks with Empirical Improvements
Questioning that the architectures designed for FP networks might not be the best for binary networks, we propose to search architectures for binary networks (BNAS) by defining a new search space for binary architectures and a novel search objective.
Factorizing Perception and Policy for Interactive Instruction Following
Performing simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for AI agents.
Learning Architectures for Binary Networks
Specifically, based on the cell based search method, we define the new search space of binary layer types, design a new cell template, and rediscover the utility of and propose to use the Zeroise layer instead of using it as a placeholder.
