v270: Proceedings of CoRL 2024

2026-06-10 · Source: Proceedings of Machine Learning Research · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, extended

Summary

Volume 270 of the Conference on Robot Learning (CoRL 2024) showcases advanced research in robot learning, emphasizing manipulation, locomotion, and navigation. A prominent trend involves integrating Large Language Models (LLMs) and Vision-Language Models (VLMs) to enable language-guided control, task planning, and zero-shot capabilities. The proceedings also feature significant developments in Reinforcement Learning and Imitation Learning for acquiring complex skills, alongside robust perception using diffusion models and 3D representations. Research further addresses critical challenges such as sim-to-real transfer, enhancing robot safety, and developing generalizable policies across diverse robot embodiments and applications, including surgical robotics and autonomous driving.

Key takeaway

Volume 270 of CoRL 2024 highlights significant advances in robot learning, focusing on real-world generalization, safety, and human-robot interaction. Papers introduce novel techniques like large language and vision-language models for zero-shot instruction following, alongside diffusion policies for dexterous manipulation and robust locomotion. These contributions accelerate the development of more adaptable, reliable, and intelligent robotic systems for complex, unstructured environments.

Topics

• Robot Manipulation
• Reinforcement Learning
• Imitation Learning
• Vision-Language Models
• Sim-to-Real Transfer

Code references

• cvlab-columbia/drrobot
• jhejna/remix
• YeWR/RLFP
• Ericonaldo/visual_wholebody
• WangYixuan12/d3fields

Best for: Research Scientist, AI Scientist, Robotics Engineer, AI Student

Related on AIssential

• v205: Proceedings of CoRL 2022 — Volume 205 of the Conference on Robot Learning, held in December 2022, showcases significant advancements across various facets of robot learning.
• v305: Proceedings of CoRL 2025 — Advancements in robot learning focus on generalization, safety, and complex interaction through large models and multi-modal sensing.
• Intelligent Robots in 2026: Are We There Yet? [Nikita Rudin] - 760 — Bridging the sim-to-real gap and integrating semantics are critical for advanced robot autonomy and value generation.
• How robots learn: A brief, contemporary history — AI models and large datasets are revolutionizing robot learning, enabling adaptation and complex task execution.
• Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs — Scene graphs provide explicit, compact memory for robots to reason over long-horizon tasks under partial observability.
• Video Friday: Digit Learns to Dance—Virtually Overnight — Advanced robotics are achieving new performance thresholds in control, learning, and human interaction.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.