Aviral Kumar

Carnegie Mellon University

収録論文 17本 ・ フィジカルAI/ロボット学習

※arXiv著者名で収集。同姓同名の別人の論文が含まれる場合があります。

論文

• 最小データでの汎用ロボットポリシーの適応
  強化学習2026/8/11

  事前学習済みのロボットポリシーが、1回のデモンストレーションと自律的なオンライン相互作用だけで新しいタスクを学習できるようにする、オフラインからオンラインへの強化学習手法MiDASを提案した。

• Adaptation of Generalist Robot Policies with Minimal Data
  2026/8/1
• BPP: Long-Context Robot Imitation Learning by Focusing on Key History Frames
  2026/2/1
• RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction
  2025/9/1
• Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance
  2024/10/1
• D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning
  2024/8/1
• Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models
  2023/10/1
• Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
  2023/9/1
• Robotic Offline RL from Internet Videos via Value-Function Pre-Training
  2023/9/1
• Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials
  2022/10/1
• Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning
  2022/7/1
• How to Leverage Unlabeled Data in Offline Reinforcement Learning
  2022/2/1
• Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
  2021/9/1
• COMBO: Conservative Offline Model-Based Policy Optimization
  2021/2/1
• Conservative Safety Critics for Exploration
  2020/10/1
• COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning
  2020/10/1
• The Reach-Avoid Problem for Constant-Rate Multi-Mode Systems
  2017/7/1