Marktechpost
OCTOBER 28, 2023
Recent developments in the field of human action recognition have enabled some amazing breakthroughs in Human-Robot Interaction (HRI). With this technology, robots have begun to understand human behavior and react accordingly. Robots must have this skill in order to dynamically localize human behaviors and work well with people.
Marktechpost
SEPTEMBER 12, 2023
The quest to make robots perform complex physical tasks, such as navigating challenging environments, has been a long-standing challenge in robotics. The primary problem this paper and article aim to address is how to efficiently teach robots these agile parkour skills, enabling them to navigate through diverse real-world scenarios.
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Marktechpost
JUNE 23, 2023
Researchers have also looked into using LLMs to improve robotic control in the area of robotics. Since low-level robot operations are hardware-dependent and frequently underrepresented in LLM training data, applying LLMs to robotics is difficult. Check Out The Paper and Project.
Marktechpost
OCTOBER 24, 2023
LIBERO, a lifelong learning benchmark in robot manipulation, focuses on knowledge transfer in declarative and procedural domains. It introduces five key research areas in lifelong learning for decision-making (LLDM) and offers a procedural task generation pipeline with four task suites comprising 130 tasks. We are also on WhatsApp.
Marktechpost
APRIL 24, 2024
Traditional robotic process automation (RPA) systems are designed for static, routine activities, falling short when unpredictability is introduced. Existing research in Robotic Process Automation (RPA) has focused on rule-based systems like UiPath and Blue Prism, which automate routine tasks such as data entry and customer service.
Marktechpost
OCTOBER 2, 2023
Robotic manipulation is advancing towards the goal of enabling robots to swiftly acquire new skills through one-shot imitation learning and foundational models. The scarcity of large and diverse robotic manipulation datasets and a reliance on visual guidance are key challenges.
Marktechpost
NOVEMBER 3, 2023
This paradigm is attractive to real-world robot learning since collecting data on a robot is expensive, and fine-tuning an existing model on a small task-specific dataset could substantially improve the data efficiency for learning a new task. These efforts, however, do not use a variety of commercial robot datasets.
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