Jeff Schneider

収録論文 38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

• FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement
  2026/7/1
• Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning
  2026/4/22
• Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning
  2026/4/1
• TADPO: Reinforcement Learning Goes Off-road
  2026/3/1
• Cost-Aware Diffusion Active Search
  2026/2/1
• Accelerated Online Reinforcement Learning using Auxiliary Start State Distributions
  2025/7/1
• Latent Policy Steering with Embodiment-Agnostic Pretrained World Models
  2025/7/1
• Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
  2025/6/1
• TD-M(PC)$^2$: Improving Temporal Difference MPC Through Policy Constraint
  2025/2/1
• Decentralized Uncertainty-Aware Active Search with a Team of Aerial Robots
  2024/10/1
• Measure Preserving Flows for Ergodic Search in Convoluted Environments
  2024/9/1
• Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization
  2024/8/1
• Planning with Adaptive World Models for Autonomous Driving
  2024/6/1
• Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving
  2024/3/1
• Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following
  2024/2/1
• Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents
  2024/1/1
• Enhancing Visual Domain Adaptation with Source Preparation
  2023/6/1
• GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search
  2023/4/1
• Exploration via Planning for Information about the Optimal Trajectory
  2022/10/1
• Cost Aware Asynchronous Multi-Agent Active Search
  2022/10/1
• Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning
  2022/7/1
• Multi-Agent Active Search using Detection and Location Uncertainty
  2022/3/1
• Robust Reinforcement Learning via Genetic Curriculum
  2022/2/1
• UGV-UAV Object Geolocation in Unstructured Environments
  2022/1/1
• An Experimental Design Perspective on Model-Based Reinforcement Learning
  2021/12/1
• Learning Cooperative Multi-Agent Policies with Partial Reward Decoupling
  2021/12/1
• Affordance-based Reinforcement Learning for Urban Driving
  2021/1/1
• Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning
  2020/11/1
• Multi-Agent Active Search using Realistic Depth-Aware Noise Model
  2020/11/1
• Interactive Visualization for Debugging RL
  2020/8/1
• Vizarel: A System to Help Better Understand RL Agents
  2020/7/1
• Asynchronous Multi Agent Active Search
  2020/6/1
• Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning
  2019/11/1
• Human Driver Behavior Prediction based on UrbanFlow
  2019/11/1
• Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions
  2019/8/1
• Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets
  2019/6/1
• Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
  2018/9/1
• Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving
  2018/8/1