A reinforcement learning framework for AI coaching, modeled as a non-cooperative game with causal skill models, shows improved human learning outcomes in a drone racing user study over baselines.
Champion-level drone racing using deep reinforcement learning
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
A 2084-parameter recurrent policy trained by distilling 1000 RL teacher policies enables zero-shot control across 10 real quadrotors differing in mass, motors, frames, propellers, and flight controllers.
RL framework for agile drone racing combines task-aware switching and physically informed procedural track generation to achieve 7.4x better zero-shot generalization to unseen tracks while maintaining competitive speeds.
GustPilot achieves 94.7% success rate in wind-disturbed quadrotor gate traversal by combining DRL velocity planning with INDI disturbance rejection, generalizing from minimal simulation training to complex real-world setups.
Reinforcement learning agents can generalize better by treating context as a first-class primitive that distinguishes slow-changing external factors from fast-changing internal ones and incorporates abstract high-level descriptors.
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
The book introduces the origins, mathematical setup, and optimization stages of RLHF including reward modeling, reinforcement learning, rejection sampling, and direct alignment algorithms.
citing papers explorer
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AI Coaching for Accelerating Human Skill Development with Reinforcement Learning
A reinforcement learning framework for AI coaching, modeled as a non-cooperative game with causal skill models, shows improved human learning outcomes in a drone racing user study over baselines.
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RAPTOR: A Foundation Policy for Quadrotor Control
A 2084-parameter recurrent policy trained by distilling 1000 RL teacher policies enables zero-shot control across 10 real quadrotors differing in mass, motors, frames, propellers, and flight controllers.
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Bridging Performance and Generalization in Reinforcement Learning for Agile Flight
RL framework for agile drone racing combines task-aware switching and physically informed procedural track generation to achieve 7.4x better zero-shot generalization to unseen tracks while maintaining competitive speeds.
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GustPilot: A Hierarchical DRL-INDI Framework for Wind-Resilient Quadrotor Navigation
GustPilot achieves 94.7% success rate in wind-disturbed quadrotor gate traversal by combining DRL velocity planning with INDI disturbance rejection, generalizing from minimal simulation training to complex real-world setups.
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Contextual Intelligence The Next Leap for Reinforcement Learning
Reinforcement learning agents can generalize better by treating context as a first-class primitive that distinguishes slow-changing external factors from fast-changing internal ones and incorporates abstract high-level descriptors.
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The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
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Reinforcement Learning from Human Feedback
The book introduces the origins, mathematical setup, and optimization stages of RLHF including reward modeling, reinforcement learning, rejection sampling, and direct alignment algorithms.