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The Ingredients of Real World Robotic Reinforcement Learning

2020-05-01 · ICLR 2020 1 · Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine

The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning. In this work, we discuss the required elements of a robotic system that can continually and autonomously improve with data collected in the real world, and propose a particular instantiation of such a system. Subsequently, we investigate a number of challenges of learning without instrumentation -- including the lack of episodic resets, state estimation, and hand-engineered rewards -- and propose simple, scalable solutions to these challenges. We demonstrate the efficacy of our proposed system on dexterous robotic manipulation tasks in simulation and the real world, and also provide an insightful analysis and ablation study of the challenges associated with this learning paradigm.

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reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)State Estimation

The Ingredients of Real-World Robotic Reinforcement Learning

2020-04-27 · Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah 외

The success of reinforcement learning for real world robotics has been, in many cases limited to instrumented laboratory scenarios, often requiring arduous human effort and oversight to enable continuous learning. In thi…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation

2023-09-25 · Patrick Lancaster, Nicklas Hansen, Aravind Rajeswaran, Vikash Kumar

Robotic systems that aspire to operate in uninstrumented real-world environments must perceive the world directly via onboard sensing. Vision-based learning systems aim to eliminate the need for environment instrumentati…

Contact-rich Manipulation Model-based Reinforcement Learning Robot Manipulation State Estimation

Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning

2024-05-30 · Davide Corsi, Davide Camponogara, Alessandro Farinelli

An exciting and promising frontier for Deep Reinforcement Learning (DRL) is its application to real-world robotic systems. While modern DRL approaches achieved remarkable successes in many robotic scenarios (including mo…

Autonomous Driving Benchmarking Deep Reinforcement Learning reinforcement-learning+1

Multimodal Deep Learning for Robust RGB-D Object Recognition

2015-07-24 · Andreas Eitel, Jost Tobias Springenberg, Luciano Spinello, Martin Riedmiller 외

Robust object recognition is a crucial ingredient of many, if not all, real-world robotics applications. This paper leverages recent progress on Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architectur…

Data Augmentation Deep Learning Multimodal Deep Learning Object+1

OffWorld Gym: open-access physical robotics environment for real-world reinforcement learning benchmark and research

2019-10-18 · Ashish Kumar, Toby Buckley, John B. Lanier, Qiaozhi Wang 외

Success stories of applied machine learning can be traced back to the datasets and environments that were put forward as challenges for the community. The challenge that the community sets as a benchmark is usually the c…