Jun 24, 2024 · SERL provides a set of libraries, env wrappers, and examples to train RL policies for robotic manipulation tasks. The following sections describe how to use SERL. We will illustrate the usage with examples.

Jan 29, 2024 · Abstract page for arXiv paper 2401.16013: SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning In recent years, significant progress has been made in the field of robotic reinforcement learning (RL), enabling methods that handle complex image observations, train in the real world, and...

Jun 24, 2024 · SERL provides a set of libraries, env wrappers, and examples to train RL policies for robotic manipulation tasks. The following sections describe how to use SERL. We will illustrate the usage with examples.

Jan 24, 2025 · hil-serl性能的一个关键方面是其高可靠性，在所有任务中均实现了100%的成功率。 作者认为这种可靠性源于强化学习固有的自我纠正能力，通过策略采样使代理能够从成功和失败中不断学习和改进。

HIL-SERL provides a set of libraries, env wrappers, and examples to train RL policies using a combination of demonstrations and human corrections to perform robotic manipulation tasks with near-perfect success rates. The following sections describe how to use HIL-SERL. We will illustrate the usage with examples. 🎬: HIL-SERL video. Table of ...

SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning，代表着机器人强化学习领域的最新进展，由加州大学伯克利分校、华盛顿大学、斯坦福大学及Intrinsic Innovation LLC联合研发。

SERL is a ready-to-use software suite for robotic RL, featuring sample efficient off-policy algorithms, various reward specification methods, and advanced controller for popular robots. It includes example tasks such as PCB assembly, cable routing, and reset-freeobject relocation.

Oct 11, 2024 · 这份名为“serl:一个用于样本高效机器人强化学习的软件套件”的文件提出了一个名为serl的软件框架，旨在促进在现实世界机器人中采用强化学习(rl)。作者认为，强化学习算法的实现细节对性能至关重要，而在现实世界的机器人系统中实现强化学习算法的复杂性 ...

Nov 21, 2024 · 通过结合强化学习运动控制策略和进化算法，serl算法成功识别出在指定设计空间内最能满足任务要求的结构参数值。与设计空间内的其他参数相比，验证了serl算法在实现理论最优值方面的卓越性能。

Feb 1, 2024 · 适应力强大： serl赋予机器人在面对未知或变化情境时的强大适应能力，使其能够完成任务。 例如，机器人学会在某个桌子上组装零件后，即使桌子位置改变，它也能够灵活调整动作，完成组装任务。