Výsledky vyhledávání - "Weaver, Catherine"

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Residual-MPPI: Online Policy Customization for Continuous Control

Autor: Wang, Pengcheng, Li, Chenran, Weaver, Catherine, Kawamoto, Kenta, Tomizuka, Masayoshi, Tang, Chen, Zhan, Wei

Policies learned through Reinforcement Learning (RL) and Imitation Learning (IL) have demonstrated significant potential in achieving advanced performance in continuous control tasks. However, in real-world environments, it is often necessary to furt

Externí odkaz: http://arxiv.org/abs/2407.00898

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BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay

Autor: Weaver, Catherine, Tang, Chen, Hao, Ce, Kawamoto, Kenta, Tomizuka, Masayoshi, Zhan, Wei

Imitation learning learns a policy from demonstrations without requiring hand-designed reward functions. In many robotic tasks, such as autonomous racing, imitated policies must model complex environment dynamics and human decision-making. Sequence m

Externí odkaz: http://arxiv.org/abs/2402.14194

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Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing

Autor: Benciolini, Tommaso, Tang, Chen, Leibold, Marion, Weaver, Catherine, Tomizuka, Masayoshi, Zhan, Wei

Autonomous racing creates challenging control problems, but Model Predictive Control (MPC) has made promising steps toward solving both the minimum lap-time problem and head-to-head racing. Yet, accurate models of the system are necessary for model-b

Externí odkaz: http://arxiv.org/abs/2311.01993

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Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning

Autor: Hao, Ce, Weaver, Catherine, Tang, Chen, Kawamoto, Kenta, Tomizuka, Masayoshi, Zhan, Wei

Hierarchical reinforcement learning (RL) can accelerate long-horizon decision-making by temporally abstracting a policy into multiple levels. Promising results in sparse reward environments have been seen with skills, i.e. sequences of primitive acti

Externí odkaz: http://arxiv.org/abs/2306.08388

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Double-Iterative Gaussian Process Regression for Modeling Error Compensation in Autonomous Racing

Autor: Su, Shaoshu, Hao, Ce, Weaver, Catherine, Tang, Chen, Zhan, Wei, Tomizuka, Masayoshi

Autonomous racing control is a challenging research problem as vehicles are pushed to their limits of handling to achieve an optimal lap time; therefore, vehicles exhibit highly nonlinear and complex dynamics. Difficult-to-model effects, such as drif

Externí odkaz: http://arxiv.org/abs/2305.07740

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Outracing Human Racers with Model-based Planning and Control for Time-trial Racing

Autor: Hao, Ce, Tang, Chen, Bergkvist, Eric, Weaver, Catherine, Sun, Liting, Zhan, Wei, Tomizuka, Masayoshi

Autonomous racing has become a popular sub-topic of autonomous driving in recent years. The goal of autonomous racing research is to develop software to control the vehicle at its limit of handling and achieve human-level racing performance. In this

Externí odkaz: http://arxiv.org/abs/2211.09378

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