Aravind Rajeswaran

収録論文 27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

• Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D
  2025/4/1
• From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control
  2024/5/1
• RoboHive: A Unified Framework for Robot Learning
  2023/10/1
• What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?
  2023/10/1
• MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation
  2023/9/1
• Train Offline, Test Online: A Real Robot Learning Benchmark
  2023/6/1
• Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
  2023/3/1
• CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning
  2022/12/1
• MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
  2022/12/1
• On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
  2022/12/1
• Real World Offline Reinforcement Learning with Realistic Data Source
  2022/10/1
• Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?
  2022/4/1
• R3M: A Universal Visual Representation for Robot Manipulation
  2022/3/1
• The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
  2022/3/1
• Policy Architectures for Compositional Generalization in Control
  2022/3/1
• Visual Adversarial Imitation Learning using Variational Models
  2021/7/1
• COMBO: Conservative Offline Model-Based Policy Optimization
  2021/2/1
• Offline Reinforcement Learning from Images with Latent Space Models
  2020/12/1
• A Game Theoretic Framework for Model Based Reinforcement Learning
  2020/4/1
• Lyceum: An efficient and scalable ecosystem for robot learning
  2020/1/1
• Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
  2018/11/1
• Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost
  2018/10/1
• Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system
  2018/3/1
• Divide-and-Conquer Reinforcement Learning
  2017/11/1
• Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
  2017/9/1
• Towards Generalization and Simplicity in Continuous Control
  2017/3/1
• EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
  2016/10/1