Search Results for author:
Found 9 papers, 3 papers with code
Superpixel Convolutional Networks using Bilateral Inceptions
We introduce a new 'bilateral inception' module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image.
Online Learning of a Memory for Learning Rates
The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself we can speed up subsequent similar learning tasks.
Leveraging Contact Forces for Learning to Grasp
While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Imitation learning allows agents to learn complex behaviors from demonstrations.
Scalable Multi-Task Imitation Learning with Autonomous Improvement
In this work, we target this challenge, aiming to build an imitation learning system that can continuously improve through autonomous data collection, while simultaneously avoiding the explicit use of reinforcement learning, to maintain the stability, simplicity, and scalability of supervised imitation.
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
Imitation learning allows agents to learn complex behaviors from demonstrations.
Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data
Similar to computer vision problems, such as object detection, Action Image builds on the idea that object features are invariant to translation in image space.
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning.
Open-vocabulary Queryable Scene Representations for Real World Planning
Large language models (LLMs) have unlocked new capabilities of task planning from human instructions.
