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Found 8 papers, 1 papers with code
Learning to Coordinate Manipulation Skills via Skill Behavior Diversification
When mastering a complex manipulation task, humans often decompose the task into sub-skills of their body parts, practice the sub-skills independently, and then execute the sub-skills together.
Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor
A noisy and diverse demonstration set may hinder the performances of an agent aiming to acquire certain skills via imitation learning.
Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning
We propose a model that learns to discover these important events and the times when they occur and uses them to represent the full sequence.
Keyframing the Future: Discovering Temporal Hierarchy with Keyframe-Inpainter Prediction
To flexibly and efficiently reason about temporal sequences, abstract representations that compactly represent the important information in the sequence are needed.
A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation
Deformable object manipulation remains a challenging task in robotics research.
Rethinking Optimization with Differentiable Simulation from a Global Perspective
In this work, we study the challenges that differentiable simulation presents when it is not feasible to expect that a single descent reaches a global optimum, which is often a problem in contact-rich scenarios.
Finding the Most Transferable Tasks for Brain Image Segmentation
Furthermore, within the same modality, transferring from the source task that has stronger RoI shape similarity with the target task can significantly improve the final transfer performance.
Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning
We aim to enable this paradigm in robotic reinforcement learning, allowing a robot to learn a new task with little human effort by leveraging data and models from the Internet.
