Karl Schmeckpeper

収録論文 23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

• Efficient Sim-to-Real Transfer of World-Action Models from Synthetic Priors
  2026/6/1
• Scaling Short-Term Memory of Visuomotor Policies for Long-Horizon Tasks
  2026/6/1
• When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning
  2026/5/1
• SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning
  2026/3/1
• You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector
  2026/3/1
• ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors
  2026/3/1
• Sceniris: A Fast Procedural Scene Generation Framework
  2025/12/1
• AnyTask: an Automated Task and Data Generation Framework for Advancing Sim-to-Real Policy Learning
  2025/12/1
• Accelerating Residual Reinforcement Learning with Uncertainty Estimation
  2025/6/1
• Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin
  2025/4/1
• On-Robot Reinforcement Learning with Goal-Contrastive Rewards
  2024/10/1
• Theia: Distilling Diverse Vision Foundation Models for Robot Learning
  2024/7/1
• Imagination Policy: Using Generative Point Cloud Models for Learning Manipulation Policies
  2024/6/1
• Cross-modal Map Learning for Vision and Language Navigation
  2022/3/1
• Uncertainty-driven Planner for Exploration and Navigation
  2022/2/1
• Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
  2021/9/1
• Learning to Map for Active Semantic Goal Navigation
  2021/6/1
• Object-centric Video Prediction without Annotation
  2021/5/1
• Reinforcement Learning with Videos: Combining Offline Observations with Interaction
  2020/11/1
• An Adversarial Objective for Scalable Exploration
  2020/3/1
• Reactive Navigation in Partially Familiar Planar Environments Using Semantic Perceptual Feedback
  2020/2/1
• Learning Predictive Models From Observation and Interaction
  2019/12/1
• RoboNet: Large-Scale Multi-Robot Learning
  2019/10/1