Abstract: Preference-based reinforcement learning (PBRL) enables policy learning through simple queries comparing trajectories from a single policy. While human responses to these queries make it possible to learn policies aligned with human preferences, PBRL suffers from low query efficiency, as policy bias limits trajectory diversity and reduces the number of … [Read more...] about DAPPER: Discriminability-Aware Policy-to-Policy Preference-Based Reinforcement Learning for Query-Efficient Robot Skill Acquisition
Trajectory
Real-Time Generation of Near Minimum-Energy Trajectories via Constraint-Informed Residual Learning: A Paradigm for Learning From Optimal Solutions
Abstract: Industrial robotics demands significant energy to operate, making energy-reduction methodologies increasingly important. Strategies for planning minimum-energy trajectories typically involve solving nonlinear optimal control problems (OCPs), which rarely cope with real-time (RT) requirements. In this article, we propose a paradigm for generating near … [Read more...] about Real-Time Generation of Near Minimum-Energy Trajectories via Constraint-Informed Residual Learning: A Paradigm for Learning From Optimal Solutions
Sequentially Teaching Sequential Tasks (ST)2: Teaching Robots Long-Horizon Manipulation Skills
Abstract: Learning from demonstration (LfD) has proved useful for teaching robots complex skills with high sample efficiency. However, teaching long-horizon tasks with multiple skills is challenging as deviations tend to accumulate, the distributional shift becomes more evident, and human teachers become fatigued over time, thereby increasing the likelihood of failure. To … [Read more...] about Sequentially Teaching Sequential Tasks (ST)2: Teaching Robots Long-Horizon Manipulation Skills
Vision-Based Policy Learning for High-Speed Autonomous Racing: A Two-Phase Learning Paradigm
Abstract: Robotic navigation in complex environments remains a critical research challenge. Traditional navigation methods focus on optimal trajectory generation within fixed free workspace, therefore struggling in environments lacking viable paths to the goal, such as disaster zones or cluttered warehouses. To address this problem, we propose AINav, an adaptive … [Read more...] about Vision-Based Policy Learning for High-Speed Autonomous Racing: A Two-Phase Learning Paradigm
Vision-Based Policy Learning for High-Speed Autonomous Racing: A Two-Phase Learning Paradigm
Abstract: Motion planning for autonomous vision-based car racing is a challenging task in robotics. Classical racing systems divide the task into numerous submodules, undermining computational efficiency and leading to error propagation. Previous studies have demonstrated impressive reinforcement learning (RL) results for end-to-end autonomous driving. However, RL exhibits … [Read more...] about Vision-Based Policy Learning for High-Speed Autonomous Racing: A Two-Phase Learning Paradigm
