Search Results for author:
Found 28 papers, 6 papers with code
D2RL: Deep Dense Architectures in Reinforcement Learning
While improvements in deep learning architectures have played a crucial role in improving the state of supervised and unsupervised learning in computer vision and natural language processing, neural network architecture choices for reinforcement learning remain relatively under-explored.
Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization
Recent works in high-dimensional model-predictive control and model-based reinforcement learning with learned dynamics and reward models have resorted to population-based optimization methods, such as the Cross-Entropy Method (CEM), for planning a sequence of actions.
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Visual Similarity plays an important role in many computer vision applications.
Ranked #13 on
Metric Learning
on CUB-200-2011
(using extra training data)
A Generative Framework for Zero-Shot Learning with Adversarial Domain Adaptation
Our approach is based on end-to-end learning of the class distributions of seen classes and unseen classes.
Ranked #1 on
Zero-Shot Learning
on CUB-200 - 0-Shot Learning
(using extra training data)
Continual Model-Based Reinforcement Learning with Hypernetworks
Effective planning in model-based reinforcement learning (MBRL) and model-predictive control (MPC) relies on the accuracy of the learned dynamics model.
Diversity inducing Information Bottleneck in Model Ensembles
Although deep learning models have achieved state-of-the-art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive uncertainty estimation are still active areas of research.
Explanations for Temporal Recommendations
Recommendation systems are an integral part of Artificial Intelligence (AI) and have become increasingly important in the growing age of commercialization in AI.
Layer-wise Relevance Propagation for Explainable Recommendations
Relationships between the images are identified by the model and layer-wise relevance propagation is used to infer pixel-level details of the images that may have significantly informed the model's choice.
A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies
Learning effective visuomotor policies for robots purely from data is challenging, but also appealing since a learning-based system should not require manual tuning or calibration.
MANGA: Method Agnostic Neural-policy Generalization and Adaptation
Efficiently transferring learned policies to an unknown environment with changes in dynamics configurations in the presence of motor noise is very important for operating robots in the real world, and our work is a novel attempt in that direction.
LEAF: Latent Exploration Along the Frontier
We target the challenging problem of policy learning from initial and goal states specified as images, and do not assume any access to the underlying ground-truth states of the robot and the environment.
Generalized Adversarially Learned Inference
Recent approaches, such as ALI and BiGAN frameworks, develop methods of inference of latent variables in GANs by adversarially training an image generator along with an encoder to match two joint distributions of image and latent vector pairs.
De-anonymization of authors through arXiv submissions during double-blind review
Under two conditions: papers that are released on arXiv before the review phase and papers that are not, we examine the correlation between the reputation of their authors with the review scores and acceptance decisions.
A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification
Clustering techniques have been proved highly suc-cessful for Takagi-Sugeno (T-S) fuzzy model identification.
Offline Policy Optimization with Variance Regularization
Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications.
Conservative Safety Critics for Exploration
Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning.
Latent Skill Planning for Exploration and Transfer
To quickly solve new tasks in complex environments, intelligent agents need to build up reusable knowledge.
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos
Learning from visual data opens the potential to accrue a large range of manipulation behaviors by leveraging human demonstrations without specifying each of them mathematically, but rather through natural task specification.
Auditing AI models for Verified Deployment under Semantic Specifications
We enable such unit tests through variations in a semantically-interpretable latent space of a generative model.
Auditing Robot Learning for Safety and Compliance during Deployment
They are also likely going to fail and be incompliant with human preferences in increasingly subtle ways.
INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL
We propose a modified objective for model-based RL that, in combination with mutual information maximization, allows us to learn representations and dynamics for visual model-based RL without reconstruction in a way that explicitly prioritizes functionally relevant factors.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective
In this work, we propose a single objective which jointly optimizes a latent-space model and policy to achieve high returns while remaining self-consistent.
CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning
On a real robot setup, CACTI enables efficient training of a single policy that can perform 10 manipulation tasks involving kitchen objects, and is robust to varying layouts of distractors.
Offline Policy Optimization in RL with Variance Regularizaton
Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications.
Zero-Shot Robot Manipulation from Passive Human Videos
Can we learn robot manipulation for everyday tasks, only by watching videos of humans doing arbitrary tasks in different unstructured settings?
Visual Affordance Prediction for Guiding Robot Exploration
Motivated by the intuitive understanding humans have about the space of possible interactions, and the ease with which they can generalize this understanding to previously unseen scenes, we develop an approach for learning visual affordances for guiding robot exploration.
RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking
The grand aim of having a single robot that can manipulate arbitrary objects in diverse settings is at odds with the paucity of robotics datasets.
Towards Generalizable Zero-Shot Manipulation via Translating Human Interaction Plans
We pursue the goal of developing robots that can interact zero-shot with generic unseen objects via a diverse repertoire of manipulation skills and show how passive human videos can serve as a rich source of data for learning such generalist robots.
