Enhancing Robotic Learning with Interactive and Data-Efficient Frameworks

The recent developments in the field of robotic manipulation and human-robot interaction have shown a significant shift towards more intuitive, data-efficient, and robust learning frameworks. A common theme across the latest research is the integration of advanced technologies such as augmented reality (AR), mixed reality (MR), and generative models to enhance the learning process and improve the quality of human demonstrations. These technologies are being leveraged to create more interactive and user-friendly interfaces, which in turn facilitate the collection of high-quality data for training robots. Additionally, there is a growing emphasis on the use of uncertainty quantification and conformal prediction to handle distribution shifts and intermittent feedback, ensuring that robots can adapt to new environments and tasks more effectively. The field is also witnessing advancements in the learning of spatial bimanual actions and affordance-centric policies, which simplify the learning process by focusing on key interaction regions of objects. Furthermore, the development of robust sim-to-real reinforcement learning techniques and the incorporation of tactile feedback are paving the way for more dexterous and contact-rich manipulation capabilities. Overall, the current direction of the field is towards creating more adaptable, intuitive, and efficient robotic systems that can learn from minimal human intervention and generalize well to new tasks and environments.

Noteworthy papers include 'ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback,' which introduces a system that uses AR and haptic feedback to guide users in collecting high-quality demonstrations, and 'Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback,' which proposes a novel uncertainty quantification algorithm to adapt the robot's uncertainty online using human feedback.

Sources

ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback

Learning Spatial Bimanual Action Models Based on Affordance Regions and Human Demonstrations

Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback

HoloSpot: Intuitive Object Manipulation via Mixed Reality Drag-and-Drop

Embodied Active Learning of Generative Sensor-Object Models

Visual-Geometric Collaborative Guidance for Affordance Learning

SDS -- See it, Do it, Sorted: Quadruped Skill Synthesis from Single Video Demonstration

DeformPAM: Data-Efficient Learning for Long-horizon Deformable Object Manipulation via Preference-based Action Alignment

Robust Manipulation Primitive Learning via Domain Contraction

OKAMI: Teaching Humanoid Robots Manipulation Skills through Single Video Imitation

Affordance-Centric Policy Learning: Sample Efficient and Generalisable Robot Policy Learning using Affordance-Centric Task Frames

Dual Action Policy for Robust Sim-to-Real Reinforcement Learning

Just Add Force for Contact-Rich Robot Policies

ALOHA Unleashed: A Simple Recipe for Robot Dexterity

Arc-Length-Based Warping for Robot Skill Synthesis from Multiple Demonstrations

RAMPA: Robotic Augmented Reality for Machine Programming and Automation

Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance

Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation

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