In recent years, researchers in the field of robotic reinforcement learning (RL) have made significant progress, developing methods capable of handling complex image observations, training in real-world scenarios, and incorporating auxiliary data such as demonstrations and previous experiences. Despite these advances, practitioners recognize the difficulty inherent in effectively utilizing robotic RL, emphasizing that the specific implementation details of these algorithms are often as crucial, if not more, to performance as the choice of algorithm in question. Yeah.
The image above shows several tasks solved using SERL in the real world. These include PCB board insertion (left), cable routing (center), and object relocation (right). SERL provides a ready-to-use package for real-world reinforcement learning, with support for efficient learning with samples, learned rewards, and restart automation.
Researchers have highlighted the significant challenge posed by the comparative inaccessibility of robotic reinforcement learning (RL) methods, hindering their widespread adoption and further development. In response to this problem, a meticulously designed library has been created. This library incorporates a sample-efficient, non-policy deep RL method and tools for reward calculation and environment restoration. Additionally, it includes a high-quality controller designed for a widely adopted robot, along with a diverse set of challenging example tasks. This resource is presented to the community as a concerted effort to address accessibility issues, offering a transparent view of its design decisions and showing compelling experimental results.
When evaluating 100 trials per task, the learned RL policies outperformed the BC policies by a large margin: 1.7 times for object relocation, 5 times for wire routing, and 10 times for PCB insertion.
The implementation demonstrates the ability to achieve highly efficient learning and obtaining policies for tasks such as PCB board assembly, cable routing, and object relocation within an average training time of 25 to 50 minutes per policy. These results represent an improvement over the most recent results reported for similar tasks in the literature.
In particular, policies derived from this implementation exhibit perfect or near-perfect success rates, exceptional robustness even under perturbations, and show emergent recovery and correction behaviors. The researchers hope that these promising results, along with the release of a high-quality open source implementation, will serve as a valuable tool for the robotics community, encouraging further advances in real-life robotics.
In short, the carefully designed library marks a critical step in making robotic reinforcement learning more accessible. With transparent design options and compelling results, it not only enhances technical capabilities but also fosters collaboration and innovation. Here's to breaking down barriers and driving the exciting future of real-life robotics!
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Janhavi Lande, Graduated in Engineering Physics from IIT Guwahati, Class of 2023. She is an upcoming data scientist and has been working in the world of ml/ai research for the last two years. What fascinates him most is this ever-changing world and its constant demand for humans to keep up. In her hobbies she likes to travel, read and write poems.
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