Search Results for author:
Found 12 papers, 3 papers with code
"Don't forget to put the milk back!" Dataset for Enabling Embodied Agents to Detect Anomalous Situations
Our work assists in this goal by enabling robots to inform their users of dangerous or unsanitary anomalies in their home.
Alexa, play with robot: Introducing the First Alexa Prize SimBot Challenge on Embodied AI
The Alexa Prize program has empowered numerous university students to explore, experiment, and showcase their talents in building conversational agents through challenges like the SocialBot Grand Challenge and the TaskBot Challenge.
Language-guided Task Adaptation for Imitation Learning
We introduce a novel setting, wherein an agent needs to learn a task from a demonstration of a related task with the difference between the tasks communicated in natural language.
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning
We propose a multimodal (vision-and-language) benchmark for cooperative and heterogeneous multi-agent learning.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Zero-shot Task Adaptation using Natural Language
Imitation learning and instruction-following are two common approaches to communicate a user's intent to a learning agent.
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards
Reinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its applicability to complex problems.
Using Natural Language for Reward Shaping in Reinforcement Learning
A common approach to reduce interaction time with the environment is to use reward shaping, which involves carefully designing reward functions that provide the agent intermediate rewards for progress towards the goal.
Nonparametric Variational Auto-encoders for Hierarchical Representation Learning
In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation space.
End to End Learning for Self-Driving Cars
The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal.
Voted Kernel Regularization
This paper presents an algorithm, Voted Kernel Regularization , that provides the flexibility of using potentially very complex kernel functions such as predictors based on much higher-degree polynomial kernels, while benefitting from strong learning guarantees.
New Rules for Domain Independent Lifted MAP Inference
In this paper, we present two new lifting rules, which enable fast MAP inference in a large class of MLNs.
