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Found 16 papers, 6 papers with code
Learning to Act with Affordance-Aware Multimodal Neural SLAM
Recent years have witnessed an emerging paradigm shift toward embodied artificial intelligence, in which an agent must learn to solve challenging tasks by interacting with its environment.
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains.
Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion
Language-guided robots performing home and office tasks must navigate in and interact with the world.
Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning
We explore possible methods for multi-task transfer learning which seek to exploit the shared physical structure of robotics tasks.
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation.
Meta Learning via Learned Loss
We present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures.
Meta-Learning via Learned Loss
This information shapes the learned loss function such that the environment does not need to provide this information during meta-test time.
Accelerating Goal-Directed Reinforcement Learning by Model Characterization
Then, we leverage this approximate model along with a notion of reachability using Mean First Passage Times to perform Model-Based reinforcement learning.
Solving Markov Decision Processes with Reachability Characterization from Mean First Passage Times
A new mechanism for efficiently solving the Markov decision processes (MDPs) is proposed in this paper.
Reachability and Differential based Heuristics for Solving Markov Decision Processes
The solution convergence of Markov Decision Processes (MDPs) can be accelerated by prioritized sweeping of states ranked by their potential impacts to other states.
Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing
We complete unseen tasks by choosing new sequences of skill latents to control the robot using MPC, where our MPC model is composed of the pre-trained skill policy executed in the simulation environment, run in parallel with the real robot.
Scaling simulation-to-real transfer by learning composable robot skills
In particular, we first use simulation to jointly learn a policy for a set of low-level skills, and a "skill embedding" parameterization which can be used to compose them.
Region Growing Curriculum Generation for Reinforcement Learning
In this work, we introduce a method based on region-growing that allows learning in an environment with any pair of initial and goal states.
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Imitation learning has traditionally been applied to learn a single task from demonstrations thereof.
Interactive Perception: Leveraging Action in Perception and Perception in Action
Recent approaches in robotics follow the insight that perception is facilitated by interaction with the environment.
Robotics
Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena
The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting congestion hotspots.
