Search Results for author:
Found 13 papers, 5 papers with code
AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent
Encouraged by the remarkable achievements of language and vision foundation models, developing generalist robotic agents through imitation learning, using large demonstration datasets, has become a prominent area of interest in robot learning.
RVT: Robotic View Transformer for 3D Object Manipulation
In simulations, we find that a single RVT model works well across 18 RLBench tasks with 249 task variations, achieving 26% higher relative success than the existing state-of-the-art method (PerAct).
Ranked #3 on
Robot Manipulation
on RLBench
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks
Meta reinforcement learning (meta-RL) aims to learn a policy solving a set of training tasks simultaneously and quickly adapting to new tasks.
Batch Reinforcement Learning Through Continuation Method
Many real-world applications of reinforcement learning (RL) require the agent to learn from a fixed set of trajectories, without collecting new interactions.
Predictive Information Accelerates Learning in RL
The Predictive Information is the mutual information between the past and the future, I(X_past; X_future).
Self-Imitation Learning via Trajectory-Conditioned Policy for Hard-Exploration Tasks
We propose a new method of learning a trajectory-conditioned policy to imitate diverse trajectories from the agent's own past experiences and show that such self-imitation helps avoid myopic behavior and increases the chance of finding a globally optimal solution for hard-exploration tasks, especially when there are misleading rewards.
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards
Reinforcement learning with sparse rewards is challenging because an agent can rarely obtain non-zero rewards and hence, gradient-based optimization of parameterized policies can be incremental and slow.
Generative Adversarial Self-Imitation Learning
This paper explores a simple regularizer for reinforcement learning by proposing Generative Adversarial Self-Imitation Learning (GASIL), which encourages the agent to imitate past good trajectories via generative adversarial imitation learning framework.
Contingency-Aware Exploration in Reinforcement Learning
This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning.
Ranked #8 on
Atari Games
on Atari 2600 Montezuma's Revenge
Self-Imitation Learning
This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions.
Ranked #3 on
Atari Games
on Atari 2600 Atlantis
Unsupervised Discovery of Object Landmarks as Structural Representations
Deep neural networks can model images with rich latent representations, but they cannot naturally conceptualize structures of object categories in a human-perceptible way.
Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
Our training objective encourages better localization on single images, incorporates text phrases in a broad range, and properly pairs image regions with text phrases into positive and negative examples.
Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
We demonstrate the ability of the model in generating 3D volume from a single 2D image with three sets of experiments: (1) learning from single-class objects; (2) learning from multi-class objects and (3) testing on novel object classes.
